Robotics

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82 papers in the last four weeks, up 413% on the four weeks before. 0.8% of all new papers.

Jul 13Week of Sep 28

Latest papers 488

Oct 7, 2026cs.RO

On-Demand Robotic Assembly via Differentiable Geometric Part Repair

Transitioning from a digital design to a robotic assembly process currently requires months of expert manual tuning to reconcile part geometries with robotic constraints. This paper presents an end-to-end, autonomous pipeline for the design and physical construction of bespoke wooden assemblies. A generative AI agent translates user prompts into initial 3D geometries, balancing the visual fidelity of the design with select physical constraints. The assemblability of the design is further improved by a gradient-based repair stage that backpropagates through a graph attention network surrogate to adjust component geometries. In addition to correcting for disjointed and overlapping components, we demonstrate hardware-specific corrections, differentiably optimizing the geometry of components to enable robot screwdriving for 86.7% of 60 novel natural language inputs, significantly outperforming prior work by a factor of ten. For ten of the structures, we physically demonstrate assemblability with two UR5e robots. This work marks a meaningful step toward on-demand robotic manufacturing, enabling the rapid production of customized, low-volume goods.

Millicent Schlafly, Fabio Schaub, Diogo Costa Pais +5

ETH Zurich, Zurich, Switzerland

Robotic Assembly Robotic Construction Robotics 3D Asset Generation

Oct 6, 2026cs.RO

Embedded Evaluation of Task Admission Coalescing in Decentralized Multi-Robot Systems

Multi-robot task allocators in dynamic missions commonly admit newly released tasks immediately, potentially invoking allocation for each new arrival. We evaluate task-admission coalescing as a mechanism for controlling allocator processor work while accounting for target-service latency, using CBAA, ACBBA, PI, and HIPC as a representative MRTA suite. The study comprises a 3,000-mission AGX Orin campaign with measured computation delay, 3,000 paired zero-compute missions, and a 96-mission Pololu 3pi+ RP2040 allocator hardware-in-the-loop campaign. Immediate (Eager) admission is compared with thresholds of two, four, and eight tasks and a four-task policy with a 10-s waiting bound across three arrival rates. Across the AGX experiments, coalescing reduces both allocator calls and processor work in 31 of 48 evaluated conditions, although the magnitude of the saving varies by allocator. The principal cost appears under sparse arrivals, where four-task batching increases online-target mean latency by 20.08-23.57 s, predominantly through admission waiting. Bounded admission reduces mean work in ten of twelve allocator-load conditions, including a 19.35% reduction with a 1.40-s mean latency increase for high-arrival HIPC. RP2040 experiments show that this tradeoff can become more favorable as processor constraints tighten. Across four matched medium-arrival HIPC scenarios, Count b=4 reduces mean RP2040 work by 59.85% and service latency by 39.47%, while the corresponding AGX cases increase latency by 27.69%. These results show that the usefulness of task-admission coalescing depends on arrival intensity, allocator-specific behavior, and the relative cost of computation on the execution platform.

James Lott, Vahraz Honary

Shiley Marcos School of Engineering University of San Diego San Diego, United States of America

Multi-Robot Systems Robotics Multi-Robot Task Allocation

Oct 6, 2026cs.RO

PhoneBot: A Low-Cost Open Humanoid Robot Platform Reusing Smartphones

The adoption of humanoid robots in education and research remains limited by high hardware costs, complex sensing systems, and substantial computational requirements. This paper presents PhoneBot, a low-cost, open-source humanoid robot platform that repurposes commodity smartphones as its primary sensing and computing unit. By using a smartphone's integrated inertial measurement unit (IMU), camera, wireless connectivity, and onboard processing capabilities, PhoneBot reduces hardware costs and simplifies the system architecture. The robot combines a modular lower-body structure driven by 13 low-cost actuators with a torso-mounted smartphone that supports perception, control computation, and user interaction. We describe the mechanical design, software architecture, and real-time communication framework that support stable locomotion and capabilities including vision-based human following, conversational interaction, filming, and mobile telepresence. Experimental evaluations demonstrate reliable walking, perception-driven interaction, and straightforward deployment using off-the-shelf consumer smartphones. With fully open-source hardware and software designs, PhoneBot provides an affordable, reproducible platform for education, research, and rapid prototyping. More details are available at https://phonebot.dev.

Ruochen Hou, Quanyou Wang, Daniel Koh +1

Robotics and Mechanisms Laboratory (RoMeLa), Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, CA 90095, USA.

Humanoid Robot Locomotion Robotics Humanoid Robotics

Oct 6, 2026cs.RO

WareFly-VLA: A Vision-Language-Action Framework for UAV Navigation and Human Tracking in Smart Warehouses

Vision-Language-Action (VLA) models have achieved impressive results in robotic manipulation and ground-mobile navigation, yet language-conditioned control of unmanned aerial vehicles (UAVs) in smart warehouses remains largely unexplored, hindered by the lack of benchmarks that jointly provide continuous low-level flight actions, fine-grained natural-language target descriptions, and realistic industrial environments. This paper introduces WareFly-VLA, a photorealistic UAV VLA framework and dataset for language-guided human search, localization, and tracking in warehouse environments. It contains 507 human-teleoperated flight episodes and 8,504 high-resolution RGB transitions collected in NVIDIA Isaac Sim, each paired with a human-written appearance description of the target worker and a synchronized four-degree-of-freedom control command. Two aerial tasks are covered: target approach and person following, under occlusion, long-range search, altitude variation, and clutter. A unified benchmark of four open-source VLA architectures (SmolVLA, GR00T N1.7, pi_0 and OpenVLA) is established under a leakage-free episode-level protocol at two control rates. The results show that language-conditioned aerial control in warehouses is far from solved: performance drops substantially under strict generalization settings, continuous action modeling consistently outperforms discrete action tokenization, only the forward channel is reliably learnable from a single frame, and current foundation-model interfaces transfer poorly from ground and humanoid embodiments to aerial platforms. The synchronized video, language, action, pose, and difficulty annotations further support world-model research. The dataset, baselines, and evaluation protocol are released to support language-grounded aerial autonomy in smart warehouses.

Thinh D. Le, Son T. Nguyen, Duong Q. Nguyen +3

Center for AI Research, VinUniversity, Ho Chi Minh City, Vietnam · College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam · VinRobotics, Hanoi, Vietnam

Aerial Robotics UAV Navigation Robotics Vision-Language-Action Models Aerial VLN

Oct 6, 2026cs.RO

Reactive Exploration of Unknown Environments for Redundant Robots using Virtual Model Control

The exploration of confined, occluded, and partially known spaces poses significant challenges in robotic manipulation. The overall pose of the robotic arm must be carefully controlled to respect tight geometric constraints while avoiding newly discovered obstacles. We address this problem by proposing an active exploration approach for redundant robotic arms with an eye-in-hand camera configuration. Our approach navigates and acquires information in real-time based on a novel scoring method that directly selects a target voxel from the unexplored space using the robot's current state and expected information gain. To move the robot safely toward the target voxel, we utilize Virtual Model Control, which guarantees compliance and enables whole-body reactive obstacle avoidance without the need for path replanning. Simulated and real-robot experiments in both confined and open environments demonstrate the effectiveness of our approach, achieving over

90\%

mapping coverage across all tested environments in under

120

seconds without colliding with obstacles.

Alessio Canzolino, Omar Faris, Alessandro De Blasi +1

Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, Italy · Department of Engineering, University of Cambridge, UK.

Active Vision for Robot Manipulation Robotics Robotic Manipulation

Oct 5, 2026cs.RO

Toward Trustworthy Physical AI for Human Interaction

Robots are entering human spaces faster than we can establish when they deserve trust. We propose a framework for trustworthy physical AI that integrates Safety, Behavioral Intelligibility, and Perceptual Alignment across embodiment, control, cognition, and design. Trustworthiness emerges from aligning physical capabilities, observable behavior, and expectations people form during interaction.

Niccolò Pagliarani, Maximilian Stölzle, Cosima du Pasquier +6

The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy. · CSAIL, Massachusetts Institute of Technology, Cambridge, MA, USA. · CHARM Lab, Stanford University, Stanford, CA, USA. +3

Robotics Robot Safety Trust in AI Human-Robot Interaction Embodied AI

Oct 5, 2026cs.RO

Lego-Like Stiffness Configuration of Planar Compliant Modules for Task-Specific Flexible Interfaces

Compliant mechanisms provide compact and intrinsic structural compliance for regulating physical interactions between mechanisms and environments. However, different tasks demand distinct stiffness characteristics, often requiring task-specific optimization and redesign due to limited geometric design space and inherent coupling among multiple stiffness components. This paper presents a Lego-like stiffness configuration approach using stackable planar compliant modules. Three complementary module geometries are introduced, with their stiffness characteristics further regulated through beam width, plate thickness, and module orientation. A unified stiffness model is established for quantitative analysis of individual and composed modules. Further, a two-stage optimization method is presented to achieve desired stiffness profiles, combining a genetic algorithm for configuration and sequential quadratic programming for parameter refinement. Experimental verification shows deviations below 6.5% for simulated stiffness. A flexible wrist is further developed as a representative implementation, exhibiting distinct compliant and dynamic responses under different stiffness characteristics. An optimized modular composition realizes prescribed stiffness values and maintains compliant obstacle interaction during high-speed motion at 1 m/s, with a maximum tested angular compliance of approximately

15^\circ

. The proposed framework provides a systematic approach for constructing flexible interfaces with task-specific stiffness characteristics.

Siyue Yao, Xiaochi Xie, Shixuan Zhao +4

State Key Laboratory of Mechanical Systems and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China · Department of Mechanical Engineering, Stanford University, USA · Shanghai Key Laboratory of Intelligent Robotics; META Robotics Institute, Shanghai Jiao Tong University, Shanghai 200240, China +1

Mechanism Design Modular Robotics Robotics

Oct 5, 2026cs.CV

R2RI: A Multi-View Event and RGB Dataset for Robot-to-Robot Interaction

Understanding and modeling interactions between autonomous agents is a fundamental challenge in robotics, with broad implications for collaborative systems, social robotics, and human-robot coexistence. Although the study of robot interactions has emerged as a compelling research direction, progress has been severely hampered by the absence of large-scale benchmarks. In this paper, we introduce Robot-to-Robot Interaction (R2RI), the first dataset specifically designed to address the Robot-Robot Interaction (RRI) task. R2RI consists of different humanoid robots and realistic interactions modeled on real human social behaviors. Complementary viewpoints are available, \textit{i.e.}, an egocentric perspective from each robot's onboard sensors, and an exocentric perspective from external fixed cameras, thus enabling rich spatial and contextual understanding of the interaction dynamics. The dataset comprises more than

6.5

M frames and

\approx5000

videos at

120

fps, including Event and RGB domains. We investigate pros and cons of each domain, comparing state-of-the-art approaches for a number of key sensing and interaction based tasks. We publicly release the dataset and its annotations for all tasks and modalities at https://github.com/MagriniGabriele/R2RI.

Gabriele Magrini, Riccardo Catalini, Federico Becattini +4

Department of Information Engineering, University of Florence, Florence, Italy · Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, Modena, Italy · Department of Information Engineering and Mathematics (DIISM), University of Siena, Siena, Italy

Robotic Data Collection Robotics Social Robotics

Oct 5, 2026cs.RO

ArtifactArena: Evaluating Models by What They Build in the Physical World

To evaluate the frontier, we must measure models not by what they say, but by what they can engineer and build in grounded physical environments. We introduce \textsc{ArtifactArena}, an open-ended platform where models face a physically grounded hardware-software co-design challenge: engineering fully functional robots to compete in a simulated arena. We evaluate a frontier model's zero-shot, verifier guided refinement, and open-ended physical design capabilities through three harnesses that refine their bot artifacts based on text descriptions, physics simulator feedback, and gameplay data. We benchmark these capabilities with an Elo ranking of frontier models derived from head-to-head tournaments between their artifacts. By releasing this framework and tournament infrastructure for ongoing community submissions, we establish a living, non-saturating testbed to continuously measure the expanding limits of open-ended intelligence in the physical world. Please visit https://artifactarena.ai for more information.

Kushagra Tiwary*, David Mayo*, Nikhil Behari +5

InfoLab, MIT CSAIL · Camera Culture, MIT Media Lab · Department of Psychiatry, NYU Grossman School of Medicine +1

Robotics Simulation Robotics AI Agent Benchmarks Hardware-Software Co-Design

Oct 5, 2026cs.RO

Future Anchored Verification and Online Recovery for World Action Models

World action models (WAMs) have emerged as a promising paradigm for robotic manipulation. They act by first predicting how a task should be performed and then decoding the actions from that future. However, the remaining actions are invalid once execution drifts from the prediction. Simply replanning from the already out of distribution state rarely restores what the task still requires; existing execution monitors decide when to stop, but not what to restore. We observe that the answer is already in hand: the future the WAM predicted before acting depicts exactly the states it intended to pass through. We introduce FAVOR (Future Anchored Verification and Online Recovery), a lightweight framework that keeps these predicted frames as anchors and uses them for verification and recovery. An Anchor Verifier compares each observation with its anchor, together with the executed actions, to flag deviations that break the task. Anchor-Guided Recovery uses a vision-language model to turn the flagged anchor into a short corrective instruction. Under strengthened instruction guidance, the WAM executes this instruction to return to the intended future. It then resumes the task. FAVOR raises the task success of the base WAM from 97.85% to 98.10% on LIBERO and from 72.60% to 72.98% on LIBERO-Plus without modifying the policy.

Zhibin Qin, Zhenxiong Tan, Xinchao Wang

National University of Singapore

World Models for Robotics Robotics World Action Models

Oct 5, 2026cs.RO

Infant simulator with an embodied caregiver: Generating infant-perspective touch and vision during social interaction

Early development unfolds in caregiver-infant dyads, where infants' sensorimotor streams are shaped by physical contact and face-to-face interaction. Yet developmental robotics simulators commonly model infants in isolation, limiting the study of caregiver-mediated experience. We present a caregiver-enabled extension of the Multi-Modal Infant Model (MIMo) in MuJoCo that turns MIMo into a controllable platform for replaying dyadic interaction and generating dense infant-perspective observations. The system provides (i) an articulated caregiver model compatible with MIMo morphologies, parameterized from anthropometrics and optionally resized to a recorded caregiver; (ii) a workflow to replay naturalistic caregiver-infant holding and soothing interactions; and (iii) logging and visualizing the infant's first-person multimodal experience (we show touch and vision). We showcase the tool on touch by introducing origin-aware contact logging that disambiguates self-, caregiver-, and environment-generated contact and supports aggregation into touch-rate statistics comparable to manual coding. While we showcase tactile analysis, the platform is intended more broadly as a generator of multimodal dyadic datasets (touch and egocentric vision) for modeling the development of social interaction.

Noemi Vaculinova, Matej Hoffmann

Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic

Robotics Simulation Human Behavior Simulation Synthetic Data Generation for Robotics Robotics

Oct 5, 2026cs.RO

A State Based Dispatch Controller for Hospital Delivery Robots with Shared Human and Infrastructure Resources

Robot delivery studies can overstate transport capacity when travel to pickups and human support fall outside the modeled schedule. We formulate a location aware dispatch model that couples robot admission to transporter support and shared elevators, charging, and cleaning. The model replays 33,079 observed hospital requests; missing contents, deadlines, staffing, and completion times remain explicit scenario assumptions. Two full grids compare human dispatch, a resource aware controller, and a proximity and workload benchmark in 30 paired replications per scenario. An exploratory extension tests a simpler deadline admission rule under the same operating model. Pickup travel increases demand on a pooled elevator bank. In one high staffing case, raising assumed elevator capacity from two to four reduces human only lateness from 55.11% to 3.18%, exceeding the dispatch differences. Direct policy comparisons and robot coverage distinguish admission selectivity from system service. The analysis explains why a robot can pass a deadline admission test yet delay service through staff handoffs and shared facilities. Its contribution is a reproducible evaluation of coupled dispatch workflows, with a clear separation between admission, completion, and return to availability. The results are conditional comparisons, not estimates of hospital benefit or released clinical capacity.

Krzysztof Siwek, Aleksandra Świetlicka

Faculty of Electrical Engineering Warsaw University of Technology Koszykowa 75, 00-662 Warszawa · Faculty of Control, Robotics and Electrical Engineering Poznań University of Technology Piotrowo 3a, 61-138 Poznan

Robotics Human-Robot Collaboration Multi-Robot Task Allocation

Oct 5, 2026cs.RO

Transporting Unsecured Stacked Payloads with a Quadrupedal Robot via Multi-Objective Reinforcement Learning

Transporting unsecured payloads with legged robots over uneven terrain requires balancing locomotion performance and payload stability, since aggressive motion can destabilize the payload even when the robot remains stable. We study quadrupedal transportation of unsecured stacked boxes on an edgeless torso-mounted board without dedicated payload sensors or active carrier mechanisms. To address this trade-off, we propose Payload-Adaptive Multi-Objective Reinforcement learning for Transportation (PAMORT). PAMORT trains a multi-objective base policy conditioned on a preference vector that weights locomotion and payload-stability reward groups, then trains a weight adjuster on the frozen policy to adapt this preference online from proprioception. In simulation, PAMORT achieves comparable or better overall transportation success than a corresponding single-objective baseline across different payload configurations, including an unseen three-box stack, despite training only with two boxes. Real-world experiments on a Unitree Go2 demonstrate zero-shot transfer to slopes and steps at or beyond the training difficulty, with mean success rates of 0.850 for PAMORT and 0.675 for the baseline across eight tasks. These results demonstrate robust unsecured-payload transportation with online adaptation of the locomotion--payload trade-off from proprioceptive information.

Nobuo Namura, Masayuki Hiromoto, Kento Uemura +2

Fujitsu Limited, Kawasaki, Japan

Multi-Objective Reinforcement Learning Robot Policy Adaptation Robotics RL for Legged Locomotion Quadruped Locomotion

Oct 5, 2026cs.RO

The Unexpired Plan: A Free Monitor for Accelerated Diffusion Policies

Training-free acceleration of a diffusion policy is accepted when an internal similarity signal reports that the shortcut changed nothing. We price every monitor a control loop can afford in closed-loop success rather than feature distance, over

114

accelerator configurations and four policy families: two accelerators each clear their own gate's bar and log every reuse as certified, while on one task one finishes every episode and the other none. A monitor is two designs, not one --- the statistic it reads, and what it does when that statistic fires. Published gates re-arm after every rejection, and under that response even an oracle handed every forward pass and the exact local action error loses twenty points; absorbing the same statistic costs one point, and most of its speed. What makes a response that never forgets affordable is a statistic that rarely fires, and what it must measure is deviation from the policy the accelerator replaced --- which a chunked policy has already paid for, its last plan not yet expired and free to read. Guarding every call this way cuts per-call compute by

1.55

--

3.09\times

, where any reference-requiring check at the same coverage would have to stop accelerating altogether. On three of our four families the schedule alone already holds the pre-stated

\pm2

-point margin. What the monitor is measurably worth shows in three places: on the fourth family, where it rescues the candidate selection landed on; on four configurations it did not select; and on a contact-rich fifth family, chosen where the schedule was expected to fail and run after every design choice was frozen, where no unmonitored arm at its speed holds the margin and the monitored one does.

Yi Zhao, Sebastian Scherer

AirLab, The Robotics Institute Carnegie Mellon University, Pittsburgh, PA, USA

Robotics Diffusion Policy Diffusion Model Inference Acceleration Runtime Verification

Oct 4, 2026cs.RO

Vela: Scaling Vision-Language-Action Models with Adaptive Action Curve Parametrization

Most vision-language-action models represent future motion as fixed-rate action chunks, tying temporal resolution and prediction horizon to a fixed output budget. This pointwise representation wastes capacity on highly correlated neighboring actions, leaves temporal continuity and smoothness to be learned implicitly, and forces a tradeoff between long-horizon coverage and the local precision required for contact-rich manipulation. To address these limitations, we introduce Vela, a vision-language-action foundation model that represents future robot behavior as continuous trajectories. Vela combines a compact spline-based action representation with motion-dependent temporal support and a shared action interface for heterogeneous embodiments, allowing a fixed output budget to adapt its temporal resolution across motions. We pretrain Vela on large-scale multi-embodiment robot data and evaluate it on LIBERO-X, EBench, and two real-world long-horizon tasks, egg-cake cooking and potato shredding, obtaining promising results across simulation and physical manipulation. These results highlight the potential of continuous action representations as a foundation for future embodied foundation models. Project page and more results: https://clementine24.github.io/Vela/ .

Yifan Li, Jiaxu Wang, Dongming Wu +4

Fudan University · Shanghai Innovation Institute · The Chinese University of Hong Kong +1

Robot Foundation Models Robotics Robotic Manipulation Vision-Language-Action Models

Oct 1, 2026cs.RO

Towards Physical Underwater Robotic Assistance for Scuba Diver Movement in Confined Spaces

Scuba divers are taught to control their depth to avoid rapid ascents and descents, which could result in serious injuries such as gas embolisms and barotrauma. However, many underwater tasks necessitate lateral control, maintaining distance between subsea structures such as coral reefs, submerged drilling instrumentation, or unexploded ordnance. In this work, we discuss a first-of-its-kind wearable robotic solution providing thruster-actuated directional guidance to a diver, as distinct from prior propulsive-assistance exoskeletons. We introduce ``Robotic Assisted Diver Movement in Confined Spaces'' (RADMCS), a wearable robot that assists divers in maintaining a fixed distance from subsea structures by leveraging perception techniques in monocular depth estimation and force-feedback from submersible thrusters to provide haptic feedback. Its small and compact form factor creates a foundational platform that could be expanded to include more sophisticated control and navigation behaviors. We present results from Institutional Review Board (IRB) in-water studies with eight human scuba diver participants on threshold sensitivity tests in both a closed-water swimming facility and ocean environments; distance-maintaining experiments in a closed-water facility; and form, fit, and function testing in the ocean. We demonstrate that relatively low thrust values (10 percent of maximum) allow robotic direction of a human's movement using the physical sensation of the robot's guidance.

Demetrious T. Kutzke, Junaed Sattar

Robotics Underwater Robotics Assistive Robotics Human-Robot Interaction

Oct 1, 2026cs.RO

ChunkVLA-AM: Parallel Action Chunking for Vision-Language-Action Robot Control in Additive Manufacturing

Vision-language-action (VLA) models unify visual perception, language understanding, and action generation, offering new opportunities for automation in additive manufacturing (AM). However, deployment in AM remains challenging because adapting these models to unseen robot embodiments is costly, and performance can degrade under environment changes. In this work, we present a framework for deploying OpenVLA-OFT on a FAIRINO FR3 robot in a fixed AM workcell. A data pipeline converts monocular real-world demonstrations into OpenVLA-compatible TFDS/RLDS datasets to support adaptation to the FR3 embodiment. At runtime, each inference request predicts an eight-step chunk of 7-D actions. The FR3 executes each chunk open loop before capturing a new observation, providing closed-loop feedback between chunks. The system uses a cloud-edge architecture in which the FR3 client streams observations to a remote inference server through a FastAPI interface. In 42 physical A-to-B object-transfer trials, evenly split between red and blue targets, the system succeeded in 39 (92.9%). All three failures occurred during final placement, when insufficient release-height control caused the object to topple. An illumination sweep identified a low-error luminance range of 85-125 on a 0-255 scale, with the lowest mean spatial error at 95.

Zhugang Liu, Kaichuang Zhang, Jinman Zhang +7

Department of Computer Science, The University of Texas Rio Grande Valley · Department of Electrical Engineering, University of South Florida · Department of Computer Science, San Diego State University +1

Robotics Vision-Language-Action Models Action Chunking Vision-Based Robot Control

Oct 1, 2026cs.RO

ALFRED: Requirement-driven development of an open-source mobile manipulator for long-term plant monitoring

Tracking seasonal change in crops and forests requires observing the same plants repeatedly. Ground robots can do this at close range, and a manipulator gives their sensors more viewpoints. Yet the robots behind long-term field datasets are rarely released with their design files, and how a robot's own structure limits arm reach and occludes its sensors is seldom compared between builds. We present ALFRED, an open-source mobile manipulator built from commercially available components. It carries a six-degree-of-freedom arm, LiDAR, RGB-D cameras, RTK GNSS and an IMU on an Ackermann-steered base, all mounted on a reconfigurable aluminium strut frame, and runs containerised ROS software. It was developed through four builds against six requirements for repeated outdoor deployment: durability, modularity, repairability, sensing reach, endurance and reproducibility. Model-based analysis of the last three builds shows the usable share of the arm's reachable poses rising from 34.0% to 60.0% and then 66.1%, and ray casting shows that only the final build keeps the frame-mounted LiDAR's horizontal view clear both forwards and backwards. ALFRED completed a year of monthly forest surveys (528 traversals) without missing a scheduled collection. This was despite battery degradation, reconfiguration for another researcher's study, and the parallel development of ALFRED 2.0 for autonomous crop-row operation, with each switch between builds taking about six hours. The deployment also showed that mechanical modularity is only as dependable as the robot description that tracks it.

Ciarán Miceal Johnson, Christopher Quail, Garry Ellard +3

Modular Robotics Mobile Robotics Robotics Robotic Manipulation

Sep 30, 2026cs.RO

Neuro-Symbolic Predicate Learning for Semantic Safe Robot Control

As robots are increasingly deployed in everyday environments, ensuring their safety has become a central challenge. Existing methods often encode safety requirements as opaque mathematical/logical formulations or dense cost functions. While effective in specific tasks, they remain difficult to interpret, tightly coupled to individual tasks, and offer limited insight into why a robot action is considered safe or unsafe. To address this limitation, we propose ``Neuro-Symbolic Predicate Learning for Semantic Safe Robot Control'' (NEUPRO), which leverages a differentiable reasoner that can learn reusable safety representations from human-specified safety knowledge. NEUPRO allows practitioners to express task-related safety requirements as transparent symbolic rules, while enabling gradients to propagate through these rules to a feature extractor that maps raw observations to safety-relevant concepts. As a result, the learned feature extractor is (softly) grounded in human-understandable semantics, supports transparent constraint evaluation, and is transferable across tasks. By coupling interpretability with differentiability, NEUPRO moves beyond opaque cost design toward reusable safety reasoning. To evaluate NEUPRO's capability, we collect and release REASON, the first real robot benchmark dataset for interpretable robot safety specification. Experiments on REASON show that NEUPRO learns safety-critical features that generalize across tasks, mitigate the interpretability limitations of conventional black-box cost formulations, and provide explicit explanations of safety violation.

Zihan Ye, Jiayi Liu, Puze Liu +4

TU Darmstadt · AIML group · Hessian AI +5

Robotic Control Robotics Robot Safety Neuro-Symbolic Reasoning

Sep 29, 2026cs.RO

What to Attend, What to Keep: Skill-Conditioned Visuotactile Representation with Progress-Guided Event Memory

Robotic manipulation integrates vision, touch, and language, whose importance shifts across stages: vision guides reaching, while touch, through its evolution over time, decides grasping, alignment, and contact. Yet existing multi-modal manipulation policies typically use fixed temporal contexts and fusion strategies, despite shifts in what each modality contributes across different skills. We study how vision and touch should be combined at the level of primitive skills, asking what each skill needs from each sensor, and propose a skill-conditioned representation in which the queried skill conditions fusion over modality-specific short-term observation tokens while attending to a sparse event memory that retains terminal observations from the last

K

executed skills. Evaluated by skill progress estimation on three contact-rich tasks, it reduces slip-detection delay by 87% against fine-tuned SOTA progress models, twist-completion delay by 67.5% against a vision-only ablation, and progress error on a blind search task by 92% through sparse event memory. Gains concentrate exactly where completion is defined by contact or task history. More broadly, our results suggest that observation formation not only policy architecture is a central challenge in multi-modal representation. Project Website: http://what-to-attend-what-to-keep.github.io/

Amir-Hossein Shahidzadeh, Seungjae Lee, Eadom Dessalene +5

Computer Science Department, University of Maryland, College Park, MD, USA

Contact-Rich Robotic Manipulation Visuo-Tactile Representation Learning Robotics Multimodal Learning

Sep 29, 2026cs.RO

Draft: A Parametric Tool for Robot Design Exploration

Robot performance is often limited by the cost of iterating on morphology and control together, since every computer-aided design (CAD) change has to be carried into a simulation-ready model before control work begins. Co-design methods attempt to close this gap, but each uses a model generator written for a single platform or lack the use of real-world data to suggest that designs are plausible. We present Draft, a parametric generation tool whose generalized engine compiles any parametric tree of serial chains into a simulation-ready MJCF model, without CAD. It allows engineers to explore design tradeoffs through easily adjustable models and evaluate how changes influence controller performance. Draft grounds the free parameters of each design using trends fitted to a survey of

114

actuators and

49

published robot descriptions, so that a generated robot is anchored to real-world hardware. We validate those trends wholistically by building twins of four off-the-shelf robots, whose masses agree to

1.10\times

geometric mean fold error. Finally, we demonstrate how Draft exposes design tradeoffs by evaluating three quadrupeds through a two-stage reinforcement learning curriculum.

David Nguyen, Marcelo Coelho, Sangbae Kim

Department of Mechanical Engineering, Massachusetts Institute of Technology · Department of Architecture, Massachusetts Institute of Technology

Robot Morphology-Control Co-Design Robotics Simulation Robotics

Sep 29, 2026cs.RO

Recompositional Robotics: Cross-Domain, Open-set, and Lifelong Modularity Beyond Morphology

Research in modular robotics has produced capable approaches allowing a robot's morphology to change online, with recent efforts also developing approaches to decide which morphology to assume and automatically propagate that decision into the robot's motion planning and control. These approaches are powerful and increase adaptability in the field. However, an alternative objective is not to build robots whose structures can change, but robots whose fundamental capabilities can change, where capability is a joint function across several domains, including kino-dynamics, perception, compute, and high-level coordinating behaviors. A robot designed to be reconfigured across these domains has a greater capacity to alter its capability than one that can be reconfigured in a single domain. We refer to this cross-domain reconfigurability as integration span, and recognize a complementary measure of the resistance to reconfiguration, which we refer to as integration inertia. Current modular robots have reduced integration inertia in the structural domain while it remains high in the other domains that contribute to integration span. We assert that the systems that can provide the most utility through reconfiguration in practice are those maximizing span and minimizing inertia and call this general problem recompositional robotics: adaptation over a heterogeneous set of modules including hardware, software, compute, and behavior that abstracts each component by the interfaces it requires and provides such that they can be reasoned over holistically. We define the problem, ground it in two deployed systems and active research efforts, and pose open questions about the future of recompositional robotics.

Steven Swanbeck, Jonathan Salfity, Corrie Van Sice +2

Texas Robotics and the Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USA

Modular Robotics Robotics

Sep 29, 2026cs.RO

Towards Spatial Perception for Heterogeneous Robot Collaboration in Subterranean Mining Environments

The autonomous extraction of deep mineral deposits in abandoned underground mines is fundamentally a multi-agent integration problem. No single platform simultaneously offers the mobility to traverse kilometers of degraded drifts and the sensing payload required to characterize an ore body. This article presents the onboard perception pipeline that bridges two heterogeneous agents within the PERSEPHONE autonomous mining mission. Which consist of a lightweight Explorer robot that maps an unknown mine and generates a 3D scene graph of inspection targets, by running a zero-shot, vision-language semantic segmentation stack that detects mineral deposits directly from natural-language prompts. The map and the graph are then handed to a second Inspector robot, which carries an advanced sensing payload and uses them to plan close-range inspection viewpoints. We detail the complete pipeline, with emphasis on the geometric abstraction that turns raw detections into actionable inspection targets, spanning per-view bounding-box generation, cross-view box merging, plane fitting, and polygon extraction, and we report an extensive field validation in a subterranean test facility and in an active magnesite mine, covering both iron-vein and magnesite mineralization under realistic, perceptually degraded conditions.

Mario Alberto Valdes Saucedo, Akash Patel, Christoforos Kanellakis +1

Robotics and Artificial Intelligence Group, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 971 87 Luleå, Sweden

Robotics Heterogeneous Collaborative Perception

Sep 29, 2026cs.RO

From Sky to Soil: A Morphing Aerial-Ground Robot for Seed Deployment

Aerial seed broadcasting can reach remote restoration sites, but provides limited control over seed placement within the soil. This paper presents a geometry-assisted, tri-functional morphing robot that combines aerial access, ground locomotion, and controlled-depth seed embedding in a fly-drive-plant architecture. After landing, the platform reconfigures into a four-wheeled planting configuration: an electronically coupled dual-motor drive folds the rear arms outward to form ground wheels, while a descending front tray engages the propulsion motors with a drill gear train. Reusing the propulsion motors for drilling eliminates a dedicated drill drive. The planting sequence forms a hole, dispenses a seed, and allows the vehicle to reposition on the ground or return to its flight configuration. Ground mobility supports repeated planting without requiring a separate flight between adjacent sites. A companion controller issues reconfiguration, tray, and seed-gate commands, while a dedicated autopilot handles flight control. The morphing and planting mechanisms are validated using a hardware prototype that demonstrates ground repositioning and seed embedding. This proof of concept establishes a hardware basis for aerial-ground seed embedding through coordinated reconfiguration and actuator reuse.

Namai Chandra, Lining Yao

Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai 600036, India · Morphing Matter Lab, Department of Mechanical Engineering, University of California, Berkeley, CA 94720 USA

Aerial Robotics Robotics Agricultural Robotics

Sep 29, 2026cs.RO

Learning to Explore Hidden Kinematics for Articulated Object Manipulation

The kinematics of an articulated object is often ambiguous from vision alone. Interaction resolves the ambiguity, and active perception methods exploit this by searching for the single action that most sharpens a belief over the kinematic parameters at each step. Such greedy search cannot be extended over a horizon without forward models of the contact and inertial dynamics, which are themselves unknown. We instead amortize action selection into training. We maintain a belief distribution over joint type and parameters, initialized from a generative prior and updated by Bayesian filtering on the observed part motion. To condition the policy on this belief, we render it as a per-point articulation flow field, the motion that the current posterior predicts for every point on the object. Carrying the inductive bias of articulated motion, this representation generalizes better than a latent encoding of the belief or flow tracked from observation. We train the policy with reinforcement learning, rewarding the entropy that each interaction removes from the posterior, so that informative exploration becomes learned behavior rather than a search at every step. Our method outperforms previous approaches across door and drawer manipulation on the PartManip benchmark, and reaches 61.7% success on ArticuRiddle, a new dataset of objects whose appearance implies the wrong articulation, against 44.4% for the best previous method. Project Website: https://hiddenkinematics.github.io/

Ruiyao Liu, Boshu Lei, Zhuoyang Pan +1

GRASP Lab, University of Pennsylvania, Philadelphia, PA 19104, USA

RL for Robotics Active Perception Active Vision for Robot Manipulation Robotics Articulated Object Manipulation Policy Learning+1

Sep 29, 2026cs.RO

Distilling Privileged Control Barrier Functions into RGB-Only Safety Filters for Dynamic Visual Navigation

RGB-only end-to-end visual navigation policies remain vulnerable to collisions in real-world dynamic environments, motivating a dedicated safety layer. Existing visual Control Barrier Function (CBF) approaches seek to provide safety from RGB observations, but often rely on real-time rendering or explicit scene reconstruction and are primarily designed for static scenes, limiting their practicality for onboard deployment. We propose a teacher-student visual distillation framework that transfers the safety behavior of a privileged CBF teacher to an RGB-only student filter for dynamic environments. The student maps a short RGB history, robot velocity, and a nominal control action directly to a safe action, while the teacher uses ground-truth robot and obstacle states in a real-to-sim dynamic Gaussian Splatting environment. To reduce the teacher-student information gap, the teacher constructs safety constraints only from obstacles observable within the student's RGB history. It also accounts for obstacle-velocity uncertainty to improve robustness to motion variations, while action augmentation exposes the student to diverse safe and unsafe nominal actions to better capture the safety boundary. At deployment, the student requires only RGB observations and robot velocity, without explicit 3D reconstruction or online rendering. Experiments show that the proposed method outperforms visual CBF baselines and improves the safety of RGB-based navigation policies under dynamic obstacle motion. Project page: https://syeon-yoo.github.io/distill-cbf-site/.

Seungyeon Yoo, Gawon Lee, Seungwoo Jung +2

Department of Aerospace Engineering, Seoul National University, Seoul, Republic of Korea

Control Barrier Functions Robotics Safe Robot Navigation Robot Safety Dynamic Obstacle Avoidance Vision-Based Robot Control+1

Sep 29, 2026cs.RO

Closed-Form Cartesian Forward Kinetostatics for Spatial Multi-Segment Tendon-Driven Continuum Robots

Forward kinetostatics of spatial tendon-driven continuum robots typically requires a nonlinear equilibrium solve for each actuation input. This paper develops a force-to-Cartesian-configuration model with a closed-form solution in quadratures for spatial multi-segment robots under tendon actuation. The Cartesian backbone centerline and accumulated material twist serve as generalized coordinates, from which the strain measures and tendon geometry are derived. Variational equilibrium yields explicit axial and bending relations and establishes zero equilibrium material twist within the proposed model for admissible longitudinal non-helical routing. The solution is propagated segment by segment without an iterative equilibrium solve, while retaining axial deformation, spatially varying axial and bending stiffnesses and tendon-routing diameter, and segment-dependent tendon participation. Numerical comparisons with a full-strain geometric variable-strain model (GVS) yield maximum length-normalized tip-position discrepancies of 8.91 x 10^-6 and 1.01 x 10^-5 for the single- and three-segment robots, respectively. Mean evaluation times of 1.52 μs and 2.94 μs, with corresponding speedups of approximately 1864x and 3348x over the baseline, demonstrate the computational advantage of the explicit force-to-configuration mapping in the reported benchmark.

Ke Wu, Fangju Yang, Xiaohui Zhang +4

Robotics Continuum Robot Control Tendon-Driven Actuation

Sep 28, 2026cs.RO

SkillWeaver: Agentic Exploration over Neural Interaction Skills for Scalable Robot Data Generation

Large-scale demonstrations have driven unprecedented progress in robot learning, yet collecting robot data through teleoperation is expensive and difficult to scale to diverse environments and long-horizon tasks. Simulation offers a scalable alternative, but existing data-generation pipelines often rely on open-loop controllers, scripted skill sequences, or task-specific programs. We introduce SkillWeaver, an agentic framework that autonomously generates robot experience by exploring over Neural Interaction Skills (NIS): reusable, parameterized, closed-loop policies that expose learned physical interaction capabilities to a reasoning agent. Given a task and a simulated environment, a VLM agent reasons about what to do next, invokes and parameterizes NIS to interact with the environment, observes their outcomes, and generates verification, reflection, and memory to guide subsequent exploration. We instantiate NIS as reinforcement-learned policies for closed-loop, contact-rich manipulation and organize exploration as verifier-guided tree search, enabling the agent to discover successful long-horizon behaviors without relying on predetermined execution pipelines. SkillWeaver scales autonomously to 39.1K demonstrations across 14.1K scenes, which we distill into visuomotor policies. Across simulation benchmarks and real-world manipulation, training on SkillWeaver-generated experience substantially improves generalization to novel objects, spatial configurations, tasks, and environments, and enables zero- and few-shot sim-to-sim and sim-to-real transfer. Our results suggest agentic exploration over neural interaction skills as a scalable alternative for robot data generation.

He Zhu, Lusen Zhao, Kwan Man Cheng +2

Carnegie Mellon University · University of Illinois Urbana-Champaign

Robot Skill Learning Robotics Simulation Synthetic Data Generation for Robotics Robotics Robotic Manipulation Robot Policy Generalization

Sep 28, 2026cs.RO

RoboCompiler: Graph-Native Compilation of Closed-Chain Robots for Consistent Modeling, Control, and Simulation

Robots with kinematic loops, coupled actuators, and changing contacts require consistent models of configuration, motion, force, and dynamics. Yet these interfaces are often reconstructed separately for control and simulation, making closure and actuation consistency difficult to maintain. This paper presents RoboCompiler, a graph-native framework that compiles a canonical mechanism graph into a shared mechanical interface. From bodies, joints, frames, inertias, and actuator ports, it constructs closure paths and analytic residual Jacobians, then assembles feasible configurations through rank-checked continuation and correction. A tangent lift maps independent velocities to full robot and task motion, while paired actuator-port maps preserve virtual work. A constraint-curvature correction extends the reduction to accelerations and projected rigid-body dynamics, including floating-base and support modes. Cycle-local evaluation, generated Jacobians, and dependency-aware reuse enable localized updates when closure inputs change. We evaluate physical loops and task-induced constraints on a industrial excavator, Unitree Go2, Franka Panda, Kangaroo, and a six-UPS Stewart platform. High-precision constrained-dynamics and independent Pinocchio checks confirm mechanical consistency; MuJoCo and Isaac Sim/PhysX executions demonstrate task performance and model reuse under native contact. For Kangaroo, compilation reduces residual-and-Jacobian evaluation time by 96.7% and closed-loop rollout wall time by 66.8%, with dynamics and control held fixed.

Mehdi Heydari Shahna, Joongheon Kim, Jouni Mattila

Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland

Robotic Control Robotics Simulation Multibody Dynamics Robotics

Sep 28, 2026cs.RO

Robot Tool Design from Scratch via Behavior-Aware Hierarchical Optimization

The ability to design a tool for a task marks a level of intelligence beyond merely understanding, selecting, or using one. Existing methods for robotic tool design typically optimize a tool's continuous shape and action within a structure that is prescribed or generated beforehand, so the structure itself stays outside the physical optimization loop. We study task-driven tool design from scratch, where tool structure, shape, and action are all derived from the desired physical outcome. Here we show that the three elements can be designed jointly by HOT, a hierarchical optimization whose upper level searches over discrete tool structures with BASS, while lower-level physical optimization evaluates their task behavior and returns milestone progress as behavioral evidence for the search, ultimately providing jointly optimized shape and action. On four tool-use tasks with distinct physical functions, HOT discovers functional structures after evaluating only a small fraction of search spaces containing up to 56 million structures, and the subsequent refinement of their geometry lowers the task loss on all tasks while preserving success, through deformations that are functionally interpretable. Once 3D printed, the tools accomplish all tasks on a real robot with the actions found in simulation. Designing tools from required physical effects, rather than a catalog of known tools, is a step toward the open-ended tool making seen in humans and animals.

Yinghan Chen, Xiyao Tian, Yizan Dai +2

Institute for AI, Peking University. · School of Psychological and Cognitive Sciences, Peking University. · Yuanpei College, Peking University. +4

Robot Morphology-Control Co-Design Robotics Robotic Tool Use

Sep 28, 2026cs.RO

AGRO-SUVIDE: Agentic Robotics for Surgical Viscoelastic Debridement

Augmented dexterity has the potential to reduce the fatigue experienced by surgeons during repetitive surgical tasks. In this paper, we propose the first AGentic RObotics framework for SUrgical VIscoelastic DEbridement (AGRO-SUVIDE), the repeated removal of small fragments attached to a viscoelastic substrate. Leveraging the self-improving and coding capability of agents, AGRO-SUVIDE adopts a modular framework. Specifically, the demonstration analysis module automatically identifies recurring skills from a single expert demonstration, using both visual and kinematic information. The construction module then builds each skill, either as a procedural model-based skill the agent codes against a scaffolded library or as a model-free policy-based skill. At runtime, the monitoring module composes the skills into a loop-style graph sized to the number of fragments it observes, then verifies pre- and post-conditions of each skill to decide whether to advance or retry. We evaluate AGRO-SUVIDE through 340 physical trials on the da Vinci Research Kit (dVRK). AGRO-SUVIDE achieves an average single-fragment removal success rate of 85%, completing consecutive three-fragment removal at 60% and at 95% with one human intervention. It further generalizes to unseen five-fragment scenarios with an average success rate of 80% for single-fragment removal. Project page: https://surgical-robotics.github.io/AGRO-SUVIDE/

Shutong Jin, Ziyang Chen, Preethi Satish +4

Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA. · Department of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, SE 10044, Sweden. · Intuitive Surgical, Sunnyvale, CA 94086, USA.

Robot Skill Learning Robotics Learning from Demonstration Surgical Robotics

Sep 28, 2026cs.RO

Robot-Assisted Deployment and Maintenance of Inflatable Modules for Lunar Habitation: A Field Demonstration

Long-term human habitation and in-situ development on the Moon open a new era of space utilization. In this context, robots are a key technology for facilitating the construction of future human outposts. Toward the deployment and establishment of human habitation modules on the lunar surface, we propose a combined system consisting of inflatable modules and a modular, reconfigurable robotic system. This paper presents a report demonstrating various robot-assisted task executions using real hardware, namely the modular and reconfigurable robot MoonBot and the inflatable module HIDAS, to enhance the reliability of their deployment and maintenance. The demonstrated tasks include robotic inspection during inflation, module position alignment, final safety locking, and three-dimensional mapping for post-deployment maintenance. All demonstrations were conducted either in a laboratory environment or at a lunar analogue test site. Finally, lessons learned are discussed to provide essential insights for this robotic application to future lunar habitation.

Kentaro Uno, Shamistan Karimov, Ashutosh Mishra +5

Space Robotics Lab. (SRL) in Department of Aerospace Engineering, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan · Department of Electrical Engineering, Graduate School of Science and Technology, Tokyo University of Science, Noda 278-8510, Japan

Robotic Construction Modular Robotics Robotic Inspection Space Robotics Robotics

Sep 27, 2026cs.RO

Large Language Models for Model-Based Robot Design

Large Language Models (LLMs) can contribute useful engineering knowledge to robot design, but directly generated designs may rely on implicit assumptions and provide no guarantees of feasibility or optimality. These assumptions are critical because different reasonable modeling choices can materially change which designs are predicted to be feasible or optimal. We therefore present a framework that uses LLMs to construct explicit engineering models containing physical relationships, compatibility constraints, and objectives, allowing these modeling choices to be inspected and revised before formal optimization. The model can then be updated with additional engineering, manufacturer, or system-specific information before formal multi-objective optimization provides feasibility and Pareto-optimality guarantees with respect to the finalized model and specified design space. We evaluate the framework on quadcopter and line-following robot component-selection problems. Across 30 direct LLM design trials, none could be verified as feasible under the corresponding finalized model. Comparisons with an independently developed expert model and successive stages of model refinement further showed that changes in modeling assumptions substantially altered the predicted feasible and Pareto-optimal design sets. Together, these results show that using LLMs to construct explicit engineering models makes the underlying design choices available for inspection and revision before those assumptions determine the optimized designs. Explicit modeling therefore provides an interface for combining LLM-generated engineering knowledge, system-specific information, and formal design optimization.

Andrew Wilhelm, Angelina Zhao, Nils Napp

Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, 14850, United States

Large Language Model-Guided Optimization Robotics Natural Language to Optimization Modeling

Sep 24, 2026cs.RO

Markerless Multi-Modal Autonomous Robotic Inspection of Large Space Structures

Future orbital infrastructures, such as deployable antennas, solar farms, and large orbital platforms will require autonomous inspection systems able to operate with limited prior knowledge and without cooperative markers. Current on-orbit servicing approaches often rely on predefined trajectories, standard interfaces, fiducial markers or accurate target models, which limits scalability for large, heterogeneous or partially unknown structures. This paper presents a markerless autonomous robotic inspection pipeline in which 3D reconstruction is used as an inspection-support representation. The system integrates a Kinova Gen2 manipulator with an end-effector-mounted multimodal sensor head composed of an RGB-D camera, a thermal camera and a 2D LiDAR. The pipeline estimates an approximate inspection volume, generates viewpoints, plans collision-free motions with MoveIt, and synchronously records RGB-D images, thermal data, and robot poses in ROS2. Candidate reconstruction methods were evaluated to select a practical method for this pipeline, with Nerfacto used for geometric reconstruction and Thermal-Nerfacto used to demonstrate thermal-aware rendering for inspection. Validation in a Gazebo-based simulator and preliminary laboratory tests reveal that the proposed system can autonomously acquire spatially coherent inspection data and produce reconstructions suitable for visual and geometric assessment, representing a step towards inspection of large non-cooperative space structures.

Juan De Dios Alfaro, Arturo Ríos, David Rodríguez-Martínez +1

Space Robotics Laboratory, University Institute for Research in Mechatronics and Cyber-Physical Systems, Universidad de Málaga, Málaga, Spain.

Robotic Inspection Space Robotics Robotic Data Collection Robotics

Sep 24, 2026cs.RO

Anthropomimetic Soft Robotic Forearm with Independently Articulated Carpal Bones Enabling Human-Like Adaptive Stiffness Modulability

The human wrist exhibits adaptive stiffness modulability: joint stiffness anisotropy can be actively regulated through muscle co-contraction. This functionality is essential for stable manipulation, yet the underlying morphological factors remain unclear. To identify these factors, we developed an anatomically accurate anthropomimetic soft robotic forearm comprising eight independently movable carpal bones interconnected by ligaments, 22 actuated muscles, and compliant fingertips. We measured wrist joint stiffness under four muscle activation patterns across three skeletal configurations: anatomically normal carpal bones, a fused proximal carpal row, and a geometric ellipsoidal skeleton. The stiffness ellipse exhibited low stiffness along the dart-throwing motion (DTM) direction when finger muscles were activated, but high stiffness along the same direction when wrist and finger muscles were activated simultaneously. These results agree with previously reported human measurements, demonstrating that precise anatomical replication reproduces human-like stiffness modulability. Fusing the proximal carpal row eliminated the low DTM-direction stiffness under finger muscle activation, while the geometric ellipsoidal skeleton showed poor stiffness ellipse reorientation across all conditions. Carpal bone motion analysis revealed significantly opposing coupling patterns between wrist and finger muscles at the proximal carpal row, accompanied by a consistent but non-significant trend at the midcarpal joint, providing a mechanical explanation for this modulation. These findings demonstrate that carpal bone morphology plays a dominant role in human wrist stiffness modulation and provide design principles for humanoid robot wrists.

Yoshinobu Obata, Yinlai Jiang, Hiroshi Yokoi +1

Department of Mechanical and Intelligent Systems Engineering, The University of Electro-Communications, Tokyo, Japan · Center for Neuroscience and Biomedical Engineering, The University of Electro-Communications, Tokyo, Japan

Robotic Hand Design Robotics Soft Robotics Humanoid Robotics

Sep 24, 2026cs.RO

From Passive Execution to Active Exploration: Agentic Embodied Manipulation in Realistic Environments

Recent advances in agentic systems have substantially enhanced the long-horizon capability of embodied manipulation. However, many existing frameworks still follow a passive execution paradigm, which limits their applicability to real-world scenarios involving textual semantic cues, distractors, and initially invisible targets. To bridge this gap, we propose an agent-based active exploration framework that enables robots to dynamically interact with the environment rather than merely execute predefined instructions. Specifically, our framework consists of three collaborative modules: a planning module for high-level task reasoning, a perception module for visual scene understanding, and an execution module for low-level manipulation. This design allows the robot to actively acquire task-relevant information, adapt its behavior based on environmental feedback, and complete manipulation tasks under partial observability. Furthermore, we introduce a fine-grained perception-execution interleaving strategy, which tightly couples visual feedback with skill execution to improve exploration robustness. We evaluate our method on a realistic Find-and-Place task, demonstrating its effectiveness in challenging environments where target objects must be actively discovered before manipulation.

Shilin Ma, Chubin Zhang, Xulong Bai +3

Tsinghua Shenzhen International Graduate School, Tsinghua University

Long-Horizon Robotic Manipulation Active Vision for Robot Manipulation Robotics Robotic Manipulation

Sep 24, 2026cs.RO

Design and Evaluation of LLM Chaining-Based Task Planning for General Purpose Service Robots

General Purpose Service Robot (GPSR) tasks, as defined in the RoboCup@Home benchmark, require robots to interpret diverse natural language commands and generate multi-step action sequences in real home environments. Conventional Single Prompt (SP) approaches suffer from context bloat and the "Lost in the Middle" phenomenon, leading to unreliable task planning. We propose an LLM chaining architecture that separates instruction classification and action generation into two specialized stages, reducing per-inference prompt length by approximately 45% while improving planning consistency. We evaluate our method using 100 randomly generated GPSR commands across three language models spanning local open-source and frontier cloud deployment contexts. Results show consistent planning improvements over SP across all models, with gains of up to +37 percentage points on local models. Further, real-robot execution experiments on the Toyota Human Support Robot (HSR) reveal that planning success alone does not guarantee task completion, with 6 of 10 tasks completing successfully and execution-layer failures identified as the primary remaining bottleneck.

Lucas Da Mota Bruno, Jiahao Sim, Yoshinobu Hagiwara

Grad. School of Science and Engineering Soka University Tokyo, Japan · Faculty of Science and Engineering Soka University Tokyo, Japan

Large Language Model-Based Robot Planning Robotics Robot Task Planning Prompt Engineering

Sep 24, 2026cs.RO

Fly, Drive, Reconfigure: A Modular Reconfigurable Aerial-Ground Platform for Field Operations

Heterogeneous robot teams distribute complementary capabilities across specialized agents, but their physical roles and capacities typically remain fixed throughout a mission. We present HARP, a Heterogeneous Aerial Robotic modules Platform in which independently deployable aerial robots physically reconfigure to compose their capabilities for field operations. HARP comprises sensor-equipped scouts, flydrive rover modules, and task-specific payload modules. Scouts map the environment and inform an energy-aware planner that jointly selects routes and air-ground mobility modes. Rover and payload modules fly independently across terrain that constrains ground travel, then autonomously assemble into a cooperative ground vehicle for energy-efficient payload transport. Motivated by environmental sampling in remote and difficult-to-traverse regions, we evaluate HARP through field experiments spanning sensing, planning, reconfiguration, airground mobility, payload transport, and task execution. We further conduct module-level deployment tests on the Greenland Ice Sheet toward future autonomous missions. HARP demonstrates how heterogeneous robot teams can adapt not only their actions, but also how their physical capabilities are composed during a mission.

Li-Yu Lo, Yanbaihui Liu, Chengchuan Shu +3

Duke University

Modular Robotics Multi-Robot Systems Robotics

Sep 22, 2026cs.RO

Learning Air-Ground Motion Control with Temporal Mode Switching and Cross-Terrain Tracking

Passive-wheeled terrestrial-aerial bimodal vehicles (TABVs) combine aerial mobility with energy-efficient ground locomotion. However, reliable air-ground mode switching under limited onboard perception and robust ground trajectory tracking across diverse terrains remain challenging when targeting real-world applications. In this work, we propose a learning-based air-ground motion control framework for passive-wheeled TABVs: 1) a learned mode selector for autonomous air-ground motion mode switching. The selector uses historical single-point time-of-flight (ToF) measurements and robot states together with future reference information to determine the active locomotion mode. 2) a reinforcement learning control policy for trajectory tracking. The policy combines proprioceptive observations with future reference information to anticipate trajectory changes. For ground locomotion, multi-terrain training and dynamics randomization enable robust tracking across different terrains. Simulation and real-world experiments demonstrate reliable air-ground switching under limited perception and accurate ground tracking across diverse terrain conditions. The learned selector outperforms a rule-based mode selector in challenging transitions, while the ground controller achieves lower position RMSE than PID across all tested conditions and maintains decent tracking where NMPC fails. With these capabilities integrated, the system tracks a 101m air-ground trajectory through multiple autonomous mode transitions with a position RMSE of 0.08m.

Ruitian Pang, Mingrui Li, Xuanting Liu +10

The State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China. · Department of Dynamics and Control, Beihang University, Beijing 100191, China. · Harbin Institute of Technology, Harbin 150001, China. +1

RL Control Robot Trajectory Tracking Robotics Terrain-Aware Robot Locomotion

Sep 22, 2026cs.RO

Benchmarking Robots for Everyday Environments: From Lab Experiments to Real-World Operations

This study introduces an interdisciplinary framework for benchmarking robots deployed in public environments, addressing the gap between traditional laboratory metrics and real-world benchmarking requirements. We evaluate three distinct robots across diverse use cases - outdoor park cleaning, pedestrian underpass cleaning, and interactive library assistance - each representing unique challenges in public daily life. Over a three-year benchmarking process (2023-2025) comprising seven benchmarking events, a consensus workshop and six on-site evaluations (two per use case), we utilized realistic indoor and outdoor test environments to assess not only technical performance but also the broader implications of deploying robots in unstructured, human-centric settings. An expert panel, spanning robotics, human-robot interaction, safety, and economics, systematically developed and refined an evaluation concept to analyze the transition from laboratory prototypes to operational systems. Our findings highlight critical factors for successful deployment, including task fulfillment, interaction quality, safety, and economic feasibility. This work provides actionable insights for researchers and practitioners aiming to bridge the gap between robotic innovation and real-world applicability.

Raphael Memmesheimer, Martina Overbeck, Dominik Beyer +19

Autonomous Intelligent Systems, University of Bonn, Germany · FZI Forschungszentrum Informatik, Germany · School of Business and Economics, Freie Universität Berlin, Germany +8

Robotics Robot Safety Human-Robot Interaction

Sep 22, 2026cs.RO

Shaft-Configuration-Adaptive Catheter Tip Position Estimation via Motor-History Conditioned Residual Learning

Tendon-driven continuum manipulators are widely used in medical applications, where accurate tip-position estimation is essential for precise navigation and instrument positioning. However, patient anatomy and procedural setup impose task-dependent unknown shaft configurations, while friction, slack, and compliance introduce hysteresis, making tip estimation challenging. This paper presents a motor-history-conditioned gated recurrent unit (GRU) residual estimator for three-dimensional catheter tip estimation without direct shaft-configuration sensing. First, an initial multidirectional sweep strategy is applied to calibrate a geometric catheter model backbone, and encode the motor-angle and drive-torque response into a shaft-configuration context vector. During subsequent motion, the context conditions a GRU that predicts a task-space residual correcting this backbone, relying on motor measurements alone. The context remains fixed for the current shaft configuration, while the recurrent state captures the evolving actuation history. Across four disposable intra-cardiac echocardiography catheters and 16 bent shaft configurations, the method achieves 3.3mm open-loop tip RMSE, a 59% reduction relative to the constant-curvature baseline.

Peihan Zhang, Michael C. Yip, Ankur Kapoor +1

Digital Technology & Innovation, Siemens Healthineers, Princeton, NJ, USA. · Department of Electrical and Computer Engineering, University of California San Diego, United States.

Residual Learning Robotics Tendon-Driven Actuation Robot State Estimation

Sep 22, 2026cs.RO

Zephyron: Integrated Design and Analytical Evaluation of a Solar-Assisted Mobile Manipulator for Multimodal Environmental Reconnaissance and Distributed Visual Inference

Environmental reconnaissance needs mobile platforms that carry sensors, preserve measurement context, and return interpretable evidence under limited energy and communication. We present a literature-informed engineering design for Zephyron, a four-wheel rover with a front manipulator, environmental sensors, distributed computer vision, local recording, and a raised rear solar module. The design keeps the prototype layout but replaces unsupported numerical assumptions with an explicit component and geometry baseline. A reproducible search retrieved 5,000 records (4,858 unique) for screening, followed by targeted review of primary literature and manufacturer documentation. The baseline uses 165 mm wheels, a 12 kg mass budget, a 72 Wh battery-energy basis, and a 20 W photovoltaic module. With rolling-resistance coefficient 0.04, steady ascent of a 10 degree grade needs about 0.517 N m per wheel under equal load sharing. An illustrative 40 W motion load gives 1.44 h from 57.6 Wh usable energy, and a 25 percent driving duty gives 4.19 h without solar input; these are calculated scenarios, not measured performance. Sensor models show how integration time, calibration, temperature, and communication delay constrain interpretation, and a quality-aware stop-and-sample policy links these constraints to mission execution. Lightweight detectors, reference-based sensor learning, and executable data-integrity checks define a reproducible machine-learning evaluation pathway. The contribution is a traceable design and evaluation framework with editable 3D models, subsystem diagrams, and reproducible analytical data. Experimental validation is required before assigning payload, endurance, detection, or field-operating ratings.

Sabik Bin Sultan, Shafi Bin Sultan, Safwan Sadad

Bangladesh Air Force Shaheen College Kurmitola · St.Joseph Higher Secondary School · Greenland Residential School

Mobile Manipulation Mobile Robotics Robotics

Sep 22, 2026cs.RO

A Reconfigurable Bidirectional Cable-Driven Hip Exoskeleton with Swappable Bench/Backpack Dual-configuration Actuation

Hip exoskeletons provide an important hardware basis for lower-limb rehabilitation and locomotor assistance. Laboratory rehabilitation assessment and system development require substantial actuation and computing resources, whereas mobile assistance requires untethered portability. Integrating both capabilities within one reusable platform remains a central design challenge. This paper presents a reconfigurable bidirectional cable-driven hip exoskeleton platform that rapidly switches between bench-mounted and backpack-mounted actuation while sharing one cable-free wearable hip interface. The platform modularly adapts the actuation configuration, end-effector sensing path, and low-level control interface. Each cable-driven end-effector weighs 0.405 kg, excluding the cable and actuation unit, and integrates an encoder and a torque sensor; experiments validated bench-mounted admittance-based motion tracking capability and backpack-mounted open-loop torque tracking. Human-worn experiments with three healthy participants used myoMOTION to evaluate the platform's wearable-side hip-motion sensing capability, verified bench-to-backpack and backpack-to-bench motion-ready switching across 30 trials in

30.1\pm16.3

s, and formed a small-scale multimodal wearable-exoskeleton gait dataset for sensing validation and data-driven algorithm development, comprising 8 min bench-mounted treadmill records and 11 min backpack-mounted outdoor walking records. These results show that, by unifying the wearable structure, actuation interface, and sensing path, the proposed platform enables validation of the same hip exoskeleton in both bench-mounted and backpack-mounted configurations, providing reusable hardware for iterative development and applications across scenarios.

YuanLong Ji, Shuhan Xiang, Qihan Ye +1

School of Biological Science and Medical Engineering, Beihang University; the Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education; Key Laboratory of Innovation and Transformation of Advanced Medical Devices, Ministry of Industry and Information Technology; National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), Beihang University, Beijing, 100191, China

Robotics Assistive Robotics Tendon-Driven Actuation

Sep 21, 2026cs.RO

Visuomotor Robotic Pruning in Planar Orchards Using Hybrid Reinforcement Learning

Dormant tree pruning is labor-intensive yet essential for maintaining modern high-productivity fruit orchards. In this work, we focus on pruning of modern planar tree training systems - V-Trellis apples and UFO cherries - where trunks and primary branches are trained into approximately planar walls. We introduce an end-to-end pipeline to learn a closed-loop visuomotor controller for robotic pruning. This controller is trained entirely using simulation and synthetically generated data and deployed in real orchards in a zero-shot manner. The pipeline comprises synthetic generation of planar orchard tree meshes, construction of a physics-based orchard simulator, automated collection of successful pruning trajectories via motion planning, and policy learning with a novel hybrid reinforcement-learning algorithm that combines offline demonstrations with online simulated rollouts. The controller uses optical-flow inputs from a wrist-mounted camera - avoiding the need for full 3D-reconstruction - and continuously guides the cutter through cluttered branch environments to a specified cutpoint with correct tool orientation. In exhaustive simulated task-space evaluations over 3,000 pruning points, the policy attains 49.9% success on V-Trellis apples and 46.0% on UFO cherries. We validate the learned controller across 38 physical trials - comprising 28 outdoor field trials in commercial and experimental orchards and 10 indoor laboratory tests - demonstrating zero-shot sim-to-real transfer. The learned policy also outperforms a classical RRT-Connect baseline on physical hardware in laboratory trials.

Abhinav Jain, Cindy Grimm, Stefan Lee

Collaborative Robotics and Intelligent Systems (CoRIS) Institute, Oregon State University, Corvallis OR 97331, USA

Visuomotor Policy Learning Robotics Robotic Manipulation Agricultural Robotics Offline-To-Online RL

Sep 21, 2026cs.RO

Estimation and Control of Tensegrity Manipulator Kinematics based on Strut Inclination Angles

Unlike conventional rigid-link robots defined by discrete joints, continuum robots pose a fundamental challenge for expressing their complex continuous bending configurations for closed-loop control. Several modelling approaches have been proposed for conventional continuum robots, but tensegrity-based continuum robots remain largely open. Moreover, many of these approaches assume a continuous elastic backbone and are therefore not directly applicable to tensegrity manipulators, whose bodies are networks of rigid struts and tensioned cables. This work presents a reduced-order model for shape and posture control of a tensegrity-based continuum manipulator. The manipulator is modelled as a serially connected parallel-link mechanism. The proposed method is formulated as an optimization problem that uses geometric constraints of the tensegrity structure together with information from the Inertial Measurement Unit (IMU) sensors embedded in the strut elements. To the best of our knowledge, this work presents the first experimental demonstration of a real-time IMU-based shape estimation method on a full-scale tensegrity manipulator and demonstrates posture control using a simple Proportional-Integral (PI) controller. The results show that the proposed method can estimate the shape of both single-module tensegrity structures and multi-module tensegrity manipulators from arbitrary static configurations and achieve desired postures.

Tufail Ahmad Bhat, Shuhei Ikemoto

Kyushu Institute of Technology, Japan

Robotic Control Robotics Continuum Robot Control Robot State Estimation

Sep 21, 2026cs.RO

StenoVLA-3D: 3D-Aware Reasoning VLA for Navigation Through Gastrointestinal Stenoses

Autonomous endoscopic navigation requires the policy model to predict actions from texture-poor monocular observations, make safe control decisions, and retain evidence of lesions after they leave the field of view. Existing vision-language-action (VLA) models primarily rely on visual appearance and short-term context, limiting geometric grounding and episode-level reporting. We introduce StenoVLA-3D, a 3D-aware VLA framework for navigating through stenotic regions. We integrate point-maps into the Cosmos-Reason 2 backbone through learned geometry-gated fusion, and also propose a temporal state branch to model traversal progress. Our reasoning-and-action backbone predicts grounded reasoning with actions, while dedicated heads estimate stenosis shape and generate the final lesion report. We further introduce EndoCausal, an episode-level dataset with lesion annotations, actions, and temporally grounded reasoning. On 40 held-out recorded test episodes, StenoVLA-3D reaches 95.2% semantic accuracy and 83.4% action accuracy. On the physical 3-DoF endoscope, it attains 88.9% and 77.8% task success in esophageal and colonic phantoms (36 trials each), substantially outperforming the evaluated baselines.

Tamima Tabassum, Yiming Huang, Tianchun Wu +7

*Equal contributor · †Corresponding author

Endoscopy Robotics Vision-Language-Action Models VLMs for Robotics

Sep 21, 2026cs.RO

Phrase-Level Robotic Guqin Performance: Bimanual Motion Planning and Audio-Tactile Interaction Monitoring

Recent advances in humanoid robotics and embodied intelligence have enabled robots to perform increasingly complex manipulation tasks. However, musical instrument performance remains a formidable benchmark, demanding not only collision-free trajectory execution but also precise contact timing, asymmetric bimanual coordination, and target acoustic outcomes on physical instruments. The guqin, a seven-string fretless zither, presents unique manipulation challenges due to its millimetric string spacing, transient right-hand plucking, and sustained left-hand harmonic contacts. In this work, we present a physical heterogeneous dual-arm robotic system for phrase-level autonomous guqin performance. We formulate guqin playing as a hybrid discrete--continuous execution problem and develop a hierarchical planning framework that coordinates working finger assignment, configuration continuity, obstacle avoidance, and tight bimanual contact schedules across consecutive musical events. The system integrates vision-guided instrument localization, tactile-based harmonic contact monitoring, and auditory feedback-informed plucking parameter calibration. Real-world experiments on a 25-event phrase demonstrate that the system reliably executes coordinated open-string and seventh-hui harmonic sequences on a physical guqin, achieving 93.6% and 96.8% event correctness across repeated trials.

Zhen Wang, Zhiheng Chen, Tianyuan Bao +1

The Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China · The Chinese University of Hong Kong - Shenzhen, Shenzhen, China

Bimanual Robotic Manipulation Robotics Robotic Manipulation Robot Motion Planning

Sep 21, 2026cs.RO

Anticipatory Robot Goalkeeping via Monotone Optimal Stopping

Robots engaged in fast physical interactions often need to act before the intent of another agent is fully known. Anticipatory goalkeeping illustrates this challenge. Waiting provides more reliable information about the target but reduces the physical opportunity for interception, whereas acting early preserves reachability but requires initiating motion under uncertainty. Given a fixed closed-loop save controller, we formulate the decision of when to initiate motion as a policy-conditional finite-horizon optimal stopping problem. Building on this formulation, we propose monotone optimal stopping (MOS), a structured release-timing method for dynamic robotic interception. The quadruped save policy is trained with reinforcement learning, while MOS determines when the policy should be activated from the evolving robot state and target belief. Rather than predicting a release time or relying on confidence alone, MOS learns the return advantage of acting now over waiting for one more observation. We derive a direct Bellman recursion for this act-versus-wait margin and impose monotonicity only with respect to physical urgency, reflecting the irreversible loss of interception opportunity as time elapses. This structure enables early activation for dynamically demanding saves while preserving closed-loop adaptation when later observations change the predicted target. Under a single-crossing condition, MOS admits a threshold release boundary with a bounded approximation error. Extensive simulation studies show that MOS improves the mean save rate from 67.7% to 74.4% over a parameter-matched learned gate and increases reversal saves from 52.1% to 66.5%. Real-robot experiments further demonstrate rapid interception and post-release direction correction under human shot-direction feints.

Hao E. Zhang, Ruize Geng, Yisen Li +8

Carnegie Mellon University · University of Texas at Arlington · General Motors

Optimal Stopping Robotics

Sep 20, 2026cs.RO

PackLab: A Comprehensive Framework for Developing, Training, and Evaluating MLLMs in Robotic Bin Packing

Robotic bin packing requires long-horizon sequential decision-making, as each object placement affects the available space for subsequent packing. Existing methods primarily rely on hand-crafted geometric heuristics that optimize predefined objectives or reinforcement learning policies learned through trial and error over predefined training configurations. Despite recent advances in multimodal large language models (MLLMs) for this task, their potential for closed-loop sequential decisions across heterogeneous packing configurations remains underexplored. To address this gap, we introduce PackLab, a comprehensive framework for developing, training, and evaluating MLLMs for closed-loop robotic bin packing. PackLab-Suite provides a physics-based simulation platform for scalable generation of diverse training packing trajectories and evaluation of their physical outcomes. PackLab-VLM is a packing-specialized MLLM that understands the evolving object and container states to jointly select objects and predict placements in a closed-loop manner. PackLab-Bench provides standardized packing scenarios at multiple difficulty levels for systematic evaluation. Extensive experiments demonstrate that, on average, PackLab-VLM outperforms conventional packing heuristics, traditional reinforcement learning methods, and general-purpose MLLMs across object sets and container configurations, highlighting the potential of MLLMs for long-horizon robotic packing. The code, model, dataset, and benchmark are available at https://github.com/Correr-Zhou/PackLab .

Donghao Zhou, Jia-Hui Pan, Fan Zhang +6

Department of Computer Science and Engineering, The Chinese University of Hong Kong · M-A-P · Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong

Long-Horizon Robotic Manipulation Robotics Robot Manipulation Benchmarks VLMs for Robotics

Sep 20, 2026cs.RO

Design and Control of a Cable-Driven Switchable Actuator with Torque/Tension Dual Modes for Exoskeletons

Existing wearable exoskeleton architectures are typically constrained by a single mechanical output modality, providing either joint torque around an anatomical joint or linear traction along a limb-training-oriented direction, which limits adaptability to diverse training scenarios. This letter presents a cable-driven switchable actuator (CDSA) that can rapidly switch between torque and tension modes while centralizing all sensing and actuation components at the proximal drive unit. A Coupled Movable Pulley Mechanism (CMPM) provides tension amplification at the distal end-effector, while a bidirectional Cable-Driven Ratchet Mechanism (CDRM) enables mode switching and preload regulation. To eliminate the need for distal instrumentation, multi-source proximal sensors are integrated with a data-driven fusion model to estimate distal output forces. An adaptive dual-mode force control strategy based on iterative learning control (ILC) is further developed. Platform experiments demonstrate transmission efficiencies of

(92.4 \pm 2.0)\%

and

(96.5 \pm 3.3)\%

in the torque and tension modes, respectively, along with a tension amplification ratio of

2.77 \pm 0.10

under tension mode. Tracking tests on simulated knee-joint gait trajectories and short-stroke tension profiles yield stable control, with RMSEs of

(4.52 \pm 0.51)\%

and

(3.15 \pm 0.19)\%

of the uncontrolled peak value, respectively. Finally, seated human-coupled experiments validate the system's controllable force generation in both joint-torque and linear-traction application modes.

YuanLong Ji, Xu Liu, Xinyuan Cai +8

School of Biological Science and Medical Engineering, Beihang University; the Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education; Key Laboratory of Innovation and Transformation of Advanced Medical Devices, Ministry of Industry and Information Technology; National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), Beihang University, Beijing, 100191, China · School of Engineering Medicine, Beihang University, Beijing, 100191, China · School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan, 364012, China

Robotic Control Robotics Assistive Robotics Tendon-Driven Actuation Adaptive Control

Sep 20, 2026cs.DC

Conflicting Pattern Formation by Teams of Anonymous, Fully Disoriented Robots

Two groups of autonomous, anonymous, and oblivious mobile robots are deployed in the two-dimensional Euclidean plane, each assigned a distinct task. We study a setting where the two groups must simultaneously solve two conflicting pattern formation problems: the \textit{gathering problem}, where robots gather at a point not known to them a priori, and the \textit{circle formation problem}, where robots occupy distinct positions on the boundary of a circle. Although each robot knows its own task, it cannot identify other members of its group. A prior solution~\cite{Conflict-1} addressed this problem for asynchronous robots having {\it direction-only axis agreement} and {\it global weak multiplicity detection} capability available to all robots in both groups. In contrast, in this work, we consider fully {\it disoriented robots} without any axis agreement or common \textit{chirality}. We study the feasibility of a solution to this problem for {\it disoriented robots}. We propose a distributed algorithm that solves the problem for semi-synchronous disoriented robots with non-rigid movements. Our proposed algorithm assumes global weak multiplicity detection only for the gathering group, while for the circle formation group, it requires local weak multiplicity detection.

Animesh Maiti, Prakhar Shukla, Subhash Bhagat

Department of Mathematics, Indian Institute of Technology Jodhpur Jodhpur, Rajasthan, India

Multi-Robot Formation Control Mobile Robotics Robotics

Sep 17, 2026cs.RO

INSPECT: Learning Robot View Selection from Assistant Use

Robots inspecting an assembly must determine which parts are present and whether they are correctly installed. During egocentric assembly assistance, head motion and workpiece handling reveal evidence for these checks, while spoken state confirmations link observations to procedural outcomes. We introduce INSPECT, which learns robot view preferences from records of a smart-glasses assistant that answers part queries and provides next-step guidance. Presence-Invariant TwinSwap (PI-TwinSwap) calibrates object evidence through paired identity interventions. Claim-indexed supervision separates evidence requirements from camera-reproducible observation changes. Object-centered calibration adapts relative view preferences to robot poses, while clause-level screening checks predicted evidence. The robot selects views using only its current observation and known poses, without candidate images. Evaluation uses annotated assistant-video replay to simulate state feedback, without target-domain view labels for policy training. On images of physical gearbox assemblies, INSPECT achieves the highest view utility among the compared non-oracle policies and raises human-rated full verifiability from 34.8% to 41.7% compared with keeping the current view. On commercial angle-grinder recordings in IMPACT, the transferred relative-view selector increases the correct decision rate from 50.6% to 54.3% with a frozen perception head. The source code is available at https://github.com/Kratos-Wen/INSPECT.

Di Wen, Kailun Yang, Wenhao Guo +7

Karlsruhe Institute of Technology, Karlsruhe 76131, Germany · Hunan University, Changsha 410012, China · Independent Researcher

Active Perception Robotic Inspection Next-Best-View Planning Robotics Robotic Assembly Robotic Perception

Sep 17, 2026cs.RO

Bayesian Continuum Robot Dynamics and State Estimation

Recent factor graph approaches to continuum robot state estimation have been successful for quasi-static applications and spatiotemporal estimation using white-noise kinematic motion priors. However, when inertial effects are significant, these approximations may fail to capture the underlying physics, limiting accuracy during dynamic motions. In contrast, our approach approximates the Cosserat rod dynamics of continuum robots. We write inertia and damping as equivalent applied loads, so that the dynamic balance retains the algebraic form of the static one from prior work with quasi-static robots. Without backbone observations, the framework reduces to a stochastic forward simulation of the robot's motion. Given observations, it jointly refines kinematic and dynamic states and infers external loads, among other states. We validate the approach through simulation and experiments, demonstrating stochastic forward simulation as well as state estimation on tendon-driven continuum robots.

James M. Ferguson, Tucker Hermans, Alan Kuntz

Department of Electrical and Computer Engineering and Department of Computer Science, Vanderbilt University, TN, USA. · Kahlert School of Computing and Robotics Center, University of Utah, UT, USA.

Factor Graph Optimization Robotics Robot State Estimation

Sep 17, 2026cs.RO

Towards AI-enhanced control: a numerical technique for trajectory smoothing of a parallel robot for pancreatic surgery

The paper presents a numerical approach for the end-effector trajectory smoothing of a parallel robot designed for minimally invasive pancreatic surgery. The approach is tailored for real-time master-slave control architecture and uses a 3D space mouse for command input for velocity control. The trajectory smoothing is achieved by generating S-curves in the end-effector velocity fields, thus controlling the accelerations, which in turn reduces tissue trauma in the minimally invasive procedures. Real-time control is enabled by segmenting the S-curves based on the command inputs from the 3D space mouse. A special case is considered where the acceleration time is constant for all command inputs. Numeric results demonstrate stable transitions (without abrupt changes) in both the end-effector parameter space and in the active joints parameters, thereby validating the proposed approach. Further work aims to test the approach on an experimental model and integrate it into AI-based training modules.

Iosif Birlescu, Alexandru Pusca, Bogdan Gherman +4

LS2N - équipe RoMas, LS2N · CESTER, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania · École Centrale Nantes, Nantes Université, CNRS, LS2N, UMR 6004, F-44000 Nantes, France +2

Robotic Control Trajectory Optimization Robotics Surgical Robotics

Sep 17, 2026cs.RO

Spatial-Semantic Uncertainty in VLM-Based Target Search: Balancing Exploration and Identification

Robots searching for a target from a natural-language description must determine not only where to search, but also which observed candidate is the desired target. These decisions reflect two distinct sources of uncertainty - spatial uncertainty over candidate locations and semantic uncertainty over target identity - that are often conflated in VLM-based search systems. We introduce a spatial-semantic uncertainty formulation that maintains separate beliefs over each component and integrates probabilistic VLM evidence into a global target-identity posterior, including probability mass for undiscovered targets. This decomposition allows an information-theoretic planner to independently value candidate discovery and target disambiguation through spatial and semantic expected information gain (EIG), providing an explicit mechanism for trading broader exploration against earlier identification. We evaluate six VLM uncertainty-elicitation interfaces on 500 synthetic targets and show that similar recognition accuracy can conceal substantial differences in calibration and false confidence. In degraded-observation search-and-identify experiments, EIG-based planners reach confident decisions in 75.0%-92.5% of trials, compared with 20.0% for Random search, while different spatial-semantic weightings achieve comparable identification accuracy once confidence is attained. Increasing semantic emphasis reduces unnecessary exploration and VLM queries, demonstrating that explicitly planning over semantic uncertainty can accelerate target resolution without sacrificing decision quality. These results highlight the distinct roles of uncertainty representation and uncertainty-driven planning in embodied VLM systems.

Alkesh K. Srivastava, Jonathan Diller, Vijay Kumar +1

Temple University, Philadelphia, PA, 19122 USA · University of Pennsylvania, Philadelphia, PA, 19104 USA

Belief-Space Planning Vision-Language Models Informative Path Planning Robotics VLMs for Robotics

Sep 17, 2026cs.RO

Resilient Motion Planning for Free-Flying Space Robots under Actuator Failures

Free-flying robots rely on multiple thrusters to maneuver in space. If one or more of these thrusters fail, the robot may lose control authority and risk mission failure. At the same time, their free-flying nature implies that, even in the absence of actuation, they continue along (locally) straight-line trajectories. In this work we present a probabilistic, proactive, motion planning framework that explicitly accounts for actuator failures in space. We model actuator failure modes as a Markov chain and propagate the probability of successfully reaching the goal along the planning horizon. Precomputed reachable sets evaluate the robot's capabilities of reaching waypoints under potential failures and an RRT

^*

-based planner concatenates these waypoints. The resulting algorithm maximizes the overall target-reaching probability, providing maximally resilient motion plans utilizing free-flying properties. We validate our approach experimentally on a physical free-flyer platform with injected actuator failures.

Nicolas de Maddalena, Joris Verhagen, Jana Tumova

Institute for Dynamic Systems and Control, ETH Zürich, 8092 Zürich, ZH, Switzerland · Division of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden, and also affiliated with Digital Futures.

Space Robotics Robotics Motion Planning Robot Motion Planning Sampling-Based Motion Planning

Sep 17, 2026cs.RO

Execution-Aware Pre-Execution Ranking for Grasp-Conditioned Robotic Placement

A geometrically valid placement can still be difficult to execute because the selected grasp changes the required end-effector pose, collision geometry, and transport motion. Placement is formulated as a pre-execution ranking problem in which supplied grasp-placement candidates are scored before planning. The model combines a typed target-conditioned point cloud with three pose descriptors and hierarchical heads for planning success and execution success conditioned on planning. On a 30-object, 1,235-scene dataset with scene-group-held-out splits, three-seed top-1 success on covered test groups reaches 85.63 +/- 1.08% for joint selection and 79.84 +/- 0.16% for fixed-target ranking. For the designated frozen seed-42 checkpoint, top-1 success improves from 72.84% to 85.78% over full-pool cuMotion for joint ranking and from 59.65% to 79.67% for fixed-target ranking. Frozen transfer to xArm7/MoveIt requires no xArm-specific retraining. Across 27 locked cases, 13 complete end to end (48.15%). Of the 16 cases that pass Top-5 preflight and begin execution, 13 succeed (81.25%). Candidate-level deployment-feasibility prediction reaches 81.25% recall, 85.20% specificity, and 83.23% balanced accuracy.

Tianyuan Liu, Rutherford Agbeshi Patamia, Benjamin Champion +2

Deakin University, Australia

Robotic Grasping Robotics Robotic Manipulation Robot Motion Planning

Sep 17, 2026cs.RO

Quantifying Mechanical Intelligence in Legged Robots with Information Theory

Mechanical intelligence, loosely defined as the reduction in control burden afforded by a robot's physical form, has become a prominent concept in robotics, with instantiations in bioinspired robotics, soft robotics, robotic swarms, and many other areas. However, rigorous theoretical understanding and quantitative measures of mechanical intelligence have lagged behind the engineering systems that the community has developed. In this work, using modern legged robots as a benchmark and exemplar, we propose several information-theoretic metrics for quantifying mechanical intelligence. By viewing body dynamics as both a computational process and a communication channel, we show that several prior insights in legged-robot engineering can be described using information theory, and we quantify how bits are processed by mechanical modes and across robot coordinates. Specifically, we examine the trade-off between explicitly incorporating compliance through series-elastic actuation and using so-called proprioceptive, low-gear-ratio transmissions, and we explore how these mechanisms interact with control policies during locomotion. We develop these results on systems of increasing complexity: a simplified linear model of a robot-leg transmission, a nonlinear single-leg simulation, and simulated quadruped robots controlled by a learned policy while navigating challenging terrain. These results lay the groundwork for broader study of robot mechanisms and their role in embodied computation.

Zach J. Patterson

Mechanical and Aerospace Engineering, Case Western Reserve University

Sep 17, 2026cs.RO

OmniCalib: Target-Free, Task-Structured Self-Calibration for Humanoid Robots

Assembly, wear, and component replacement perturb the sensor extrinsics and joint zeros encoded by a humanoid CAD model. Existing procedures calibrate one sensor pair or require external fiducials. Using only robot-native motion and onboard sensing, we present OmniCalib, a target-free workflow that calibrates the full upper limbs---all 14 arm joint zeros and the extrinsics of both wrist and chest cameras---as well as lower limbs and the multi-camera head rig. Each module matches a robot-native task to a parameter block, checks observability, and writes only supported corrections to the CAD model. Our depth ICP method recovers all 14 arm joint zeros and calibrates all RGB-D camera extrinsics without any calibration target. Relative to CAD, the estimated extrinsic corrections are 10.56 mm and 1.74 degrees for the left wrist, 6.33 mm and 1.25 degrees for the right wrist, and 9.81 mm and 0.929 degrees for the chest RGB-D camera. ICP point-to-plane residual is 2.09 mm. On the same injected offsets, ICP and ArUco recover all 14 joint zeros below the 0.1-degree encoder-resolution reference. On an AGIBOT A3 Ultra humanoid, four static double-support stances recover all 12 lower-limb joint-zero offsets injected with an RMS error of 0.063 degrees. The head module combines multi-camera visual odometry with legged odometry and dynamic compensation through the live ROS transform tree. Using only planar walking, it attains a mean SO(3) error of 1.061 degrees across three sequences. The best sequence reaches 0.775 degrees, competitive with iKalibr at 0.902 degrees from rich 6-DOF excitation. Rig-relative angles repeat within 0.140 degrees. Injection recovery and held-out tests validate each observable block.

Kaixiang Lu, Haiyu Lan, Chunxiao Qiao +6

Robotics Camera Calibration Humanoid Robotics

Sep 16, 2026cs.RO

Self-excited actuation enables adaptive and resilient flapping-wing flight

The muscles that power insect flight fall into one of two categories: 1) synchronous muscles that contract under direct control from the nervous system, and 2) asynchronous muscles which have an intrinsic stretch activation response that spontaneously generates wingbeats without the need for signaling from the brain. It is thought that the emergent nature of asynchronous wingbeats provides both adaptive and responsive capabilities for flight control. To date, most flying robots use synchronous actuation. In this paper we develop the first flight-capable flapping wing robot that uses asynchronous actuation. We demonstrate that asynchronous actuation allows wings to respond to changes in the resonant mechanics of the body without control input, and wings can react instantaneously to collisions with obstacles with no extrinsic sensing needed. Flight tests within cluttered environments demonstrate that asynchronous actuation significantly improves stability and performance when compared to synchronous actuation. In total this work demonstrates that a flapping wing robot actuation strategy that emulates the asynchronous muscles of flying insects can provide fast, reactive actuation responses before a control system would need to intervene. This partitioning of embodied control to both the low-level actuation dynamics and and high-level sensorimotor system provides a compelling blueprint for new flying robots.

Rundong Yang, Ethan S. Wold, Ellen Liu +5

Mechanical and Aerospace Engineering, University of California San Diego, San Diego, CA, 92161, USA · School of Biological Sciences and School of Physics, Georgia Institute of Technology, Atlanta, GA, 30332, USA · Mechanical Industrial Engineering, Montana State University, MT, 59717, USA

Robotic Control Aerial Robotics Robotics