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

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Latest papers 488

May 18, 2026cs.RO

Adversarial Stress Testing of SPARK Humanoid Safety Filters

Humanoid robots are difficult to deploy safely because they have high-dimensional bodies, many collision constraints, and must operate near people and obstacles. Safety filters help by modifying a nominal control action when it may violate collision-avoidance constraints. Still, nominal benchmark scores do not fully show how these filters behave in harder environments. In this work, we study the robustness of SPARK humanoid safety filters through replication and stress testing. We replicate the SPARK benchmark case G1SportMode_D1_WG_SO_v1 in MuJoCo and evaluate RSSA, RSSS, SSA, CBF, PFM, and SMA under controlled random seeds. We also built a post-processing pipeline that converts raw SPARK logs into goal-tracking, minimum-distance, and collision-step metrics. Our results show that some methods track the goal more closely, while others reduce collision steps more effectively. The stress tests further indicate that safety behavior can change under obstacle crowding, noisy distance estimates, and delayed obstacle information. These findings suggest that humanoid autonomy should be evaluated beyond nominal performance, using metrics that expose failure modes before deployment.

Saurav Ghosh, Abdou Sow, Luke Zhang

aDepartment of Computer Science and Engineering, Washington University in St. Louis, Missouri, United States

Adversarial Robustness Robotics Robot Safety Safety Filtering AI Safety Evaluation Humanoid Robotics+1

May 18, 2026cs.RO

Confidence-Gated Robot Autonomy: When Does Uncertainty Actually Help?

Robotic systems often use predictive uncertainty to decide whether to act autonomously or defer to a fallback policy. In threshold-gated autonomy, uncertainty matters mainly through its ability to rank likely errors. Standard metrics such as expected calibration error and AUROC do not directly test whether uncertainty changes act/defer decisions. We therefore evaluate uncertainty using Spearman rank correlation, paired bootstrap equivalence testing, and act/defer agreement. Across three temporal activity-recognition benchmarks, we find a dataset-dependent competence regime below which uncertainty provides a weak and unstable error ranking. Above this regime, softmax heuristics, MC Dropout, and ensembles produce similar gating behavior, while threshold choice has a much larger effect on execution outcomes. A multi-seed embodied simulation shows the same pattern for collision rate and cost once realized autonomy is matched. Under temporal covariate shift, ranking quality remains stable, but fine grained semantic OOD detection remains near chance. These results suggest that simple uncertainty proxies can suffice for selective gating once the base model is competent, but not for semantic novelty detection.

Johannes A. Gaus, Jhon P. F. Charaja, Daniel Haeufle

Hertie Institute for Clinical Brain Research & Center for Integrative Neuroscience, University of Tübingen, Germany

Uncertainty Quantification Selective Prediction Robotics Decision-Making under Uncertainty

May 18, 2026cs.RO

A Dexterous and Compliant Gripper With Soft Hydraulic Actuation for Microgravity Manipulation

Astrobee's existing one-degree-of-freedom (DOF) underactuated compliant claw gripper enables perching on the International Space Station (ISS), but provides limited capability for continuous dexterous manipulation. More complex microgravity tasks require an end-effector that can maintain stable contact while limiting disturbance to the free-flying base, since contact forces directly couple into base motion. This article presents the integration of DexCoHand, a dexterous and compliant two-finger, 6-DOF gripper, with the Astrobee free-flying robot for microgravity manipulation. The system is evaluated in MuJoCo using Astrobee's standard handrail perching sequence, including approach, perching, and subsequent pan and tilt motions. Compared with Astrobee's existing gripper, DexCoHand preserves the commanded pan and tilt motions while reducing unintended cross-axis base motion. Hardware experiments on Earth further demonstrate DexCoHand's dexterous manipulation capabilities and its potential for more adaptable intelligent manipulation tasks.

William Su, Jordan Kam, Yixiao Wang +1

Aerospace Engineering Program, University of California, Berkeley, Berkeley, CA 94720, USA. · Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA 94720, USA. · Department of Mechanical Engineering, National University of Singapore, Singapore 117575.

Dexterous Manipulation Space Robotics Robotics Robotic Manipulation Soft Robotics

May 16, 2026cs.RO

SADP: Subgoal-Aware Diffusion Policy for Explainable Robots Learned from Foundation Model Generated Demonstrations

Explainable robots require not only successful task execution but also the ability to expose internal decision-making process in a user-friendly manner. However, most imitation learning methods are trained solely on task-level demonstrations, without explicitly modeling subgoal structure or execution progress. This limitation is further exacerbated by the scarcity of subgoal-level supervision in standard robot learning datasets, which restricts the development of robots that can convey the subtasks they are executing during long-horizon manipulation. To address this issue, this paper proposes Subgoal-Aware Diffusion Policy (SADP), a framework that leverages foundation models to autonomously generate subgoal-annotated demonstrations and trains diffusion policies on these datasets. SADP structures policy execution around human-interpretable subgoals by conditioning action generation on both task-level and subgoal-level descriptions. A lightweight auxiliary head further predicts subgoal completion states, allowing the robot to expose its current execution stage and monitor subgoal progression. Experiments in RLBench simulations and real-world evaluations on a UR5e robot demonstrate that SADP achieves higher task success rates than strong task-conditioned diffusion baselines, while providing subgoal-level execution signals for monitoring progress and diagnosing failures. These results highlight that built-in, rather than post-hoc, interpretability can coexist with high task performance.

Site Hu, Takato Horii

Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, Osaka, Japan

Robot Policy Learning Long-Horizon Robotic Manipulation Robot Skill Learning Robotics Diffusion Policy Robot Imitation Learning

May 15, 2026cs.RO

Nori Bot: A Sub-$1,000 Floor-to-Counter Mobile Manipulator

Open-source mobile manipulators have reached

660 (XLeRobot) but every sub-

1,000 platform shares three limitations: a fixed-height workspace, reactive-only control, and no protection against the stall-induced burn-out that destroys cheap Feetech servos. We present Nori Bot, a 17-DoF dual-arm mobile manipulator at $947 (~3% the cost of comparable commercial platforms) that addresses all three: (1) a 600mm Z-axis lift on the existing servo bus for floor-to-counter reach; (2) a thin-client Raspberry Pi 4 paired with the OpenClaw proactive agent runtime so cron jobs and hooks trigger physical tasks autonomously; and (3) a software safety stack with sensorless grip-force feedback via motor current on a soft TPU finger. Code, CAD, and the skill manifest will be released.

Antonio Li, Sungjoon Park, Wen Ni Chew

Department of Computer Science, Columbia University

Robotic Control Mobile Manipulation Robotics Robotic Manipulation

May 15, 2026cs.RO

Designing for Robot Wranglers: A Synthesis of Literature and Practice

Robots are increasingly present in human spaces, such as for conducting deliveries in hospitals, interacting with visitors at museums, and stocking items in warehouses. To ensure the seamless integration of robots into these spaces, a new role in human-robot interaction is emerging - the robot wrangler, namely an individual who is responsible for setting up, overseeing, and troubleshooting the robot. To understand the needs of this stakeholder, we conducted a scoping review that uncovered a typology of robot wrangling across the research literature, and discovered that wrangling is an umbrella term that collapses a highly complex and heterogeneous space of activities, often rendering this labor difficult to characterize and support. To further clarify and understand robot wrangling, we then reflected on our own firsthand and imagined experiences as robot wranglers within our own respective domains. Guided by the scoping review and our reflections, we devise a series of design implications for supporting wranglers directly as individuals and as members of a wider service ecology.

David Porfirio, Ian McDermott, Hsin-Mei Chen +3

Computer Science Department George Mason University Fairfax, VA, USA · Department of Computer Science/Department of Art University of Maryland College Park, MA, USA · Houston Methodist Houston, TX, USA +3

Robotics Human-Robot Interaction

May 15, 2026cs.RO

Lamarckian Inheritance in Dynamic Environments: How Key Variables Affect Evolutionary Dynamics

The co-optimization of a robot's body and brain presents a coupled challenge: the morphology constrains which control strategies are effective, while the control determines how well the morphology performs. To address this, we combine morphology optimization as evolution with controller optimization as lifetime learning, utilizing Lamarckian inheritance to transfer learned controller parameters from parent to offspring. In dynamic environments, existing literature presents conflicting evidence: while traditional evolutionary theory often suggests Lamarckian inheritance lacks benefit, recent studies in evolutionary robotics indicate it can improve performance. We hypothesize that this is because previous works have not included all relevant variables with dynamic environments. In this work, we show that the benefit of Lamarckian inheritance depends on two variables: how conflicting the environmental changes are to robot control, and the predictability of those changes for the robotic agent. Using virtual soft robots and two different learning approaches, Bayesian optimization and reinforcement learning, we show that Lamarckian inheritance only underperforms Darwinian inheritance when the changes are both conflicting and unpredictable. We find that adding a sensor to detect environmental changes restores the benefits for Lamarckian inheritance in conflicting environments, by allowing robotic agents to predict the need for a different behavior, thereby generalizing their control.

K. Ege de Bruin, Kyrre Glette, Kai Olav Ellefsen

Department of Informatics, University of Oslo, Norway · RITMO, University of Oslo, Norway

Robotics Evolutionary Robotics

May 15, 2026cs.RO

Combinatorial Optimization of Robotic Hand Kinematic Structures Using a Potential-Dexterity-Based QUBO Formulation

Robotic hand kinematic design involves combinatorial selection among multiple finger structures whose performance depends on both individual kinematic characteristics and inter-finger interactions. This study presents a potential-dexterity-based quadratic unconstrained binary optimization (QUBO) formulation for combinatorial optimization of robotic hand kinematic structures. Potential dexterity-related metrics were evaluated through kinematic analysis and encoded into a 27-variable QUBO problem incorporating individual finger performance, thumb-to-finger workspace-overlap interactions, one-hot selection constraints, and pairwise structural compatibility. Exhaustive evaluation of the design space established a reference optimum and enabled direct validation of the formulated objective. Discretization sensitivity analysis showed that the selected design remained unchanged across the tested joint-space sampling intervals and at a finer workspace voxel resolution, while sensitivity was observed under coarse voxelization. Simulated annealing consistently recovered the reference optimum at higher sampling counts, and direct execution on D-Wave quantum annealing hardware also recovered the reference optimum under all tested sampling conditions. These results demonstrate that the proposed formulation provides a common QUBO representation for constrained robotic hand kinematic structure selection and enables evaluation using exhaustive, classical annealing, and quantum annealing approaches.

HyoJae Kang, Yeong Jae Park, Jeongdo Ahn +1

Advanced Robotics Research Center, Korea Institute of Machinery & Materials (KIMM) · Mechanical Engineering, University of Science & Technology (UST)

Robotic Hand Design Quantum Annealing Combinatorial Optimization Robotics Quadratic Unconstrained Binary Optimization

May 15, 2026cs.RO

Hybrid LLM-based Intelligent Framework for Robot Task Scheduling

This study introduces intelligent frameworks that use Large Language Models (LLMs) to improve task scheduling for construction robots. The LLM is fed with key data about the desired task, such as agent action abilities, and the desired end goal to be achieved. A well-balanced allocation strategy is developed, optimizing both time efficiency and resource utilization. Our system utilizes a Natural Language Processing interface to streamline communication with construction professionals and adapt in real-time to unexpected site conditions. We concurrently use two LLM agents, specifically generator (GPT-4) and supervisor (Gemma 3/Llama 4/Mistral 7b) LLM agents to provide a more precise task schedule. We evaluate the proposed methodology using a straightforward scenario and provide metric scores to prove the efficacy of the frameworks. Our results highlight that the implementation of LLMs is crucial in construction operational tasks including robots.

Swayamjit Saha, Subhabrata Das, Haonan Duan +1

Department of Computer Science and Engineering, Mississippi State University, Starkville, MS, USA · 2Graduate School of Arts and Sciences, Columbia University, New York, NY, USA · 3Consumer and Community Banking, JPMorgan Chase, New York, NY, USA +2

Robotic Construction Large Language Model-Based Robot Planning LLM Agent Orchestration Robotics

May 14, 2026cs.RO

MR-SLAM: Immersive Spatial Supervision for Multi-Robot Mapping via Mixed Reality

Operating a multi-robot fleet for simultaneous localization and mapping (SLAM) in applications such as building inspection or warehouse-aisle monitoring requires the operator to maintain spatial awareness of each robot's position and mapping state, a task that scales poorly on conventional 2D interfaces. We present MR-SLAM, a mixed reality (MR) system in which an operator wearing a Meta Quest 3 headset teleoperates three simulated TurtleBot3 robots through a passthrough view with real-world occlusion, while spatially anchored dashboard panels report mapping progress in situ. Each robot runs an independent SLAM Toolbox instance whose occupancy grid is merged in real time on a Robot Operating System 2 (ROS 2) back end. Across five 9-minute evaluation sessions, the system delivered scans at 8.83 +/- 0.16 Hz, mapped 17.9 +/- 0.8 m^2 of merged occupancy, and reached 94.7 +/- 0.5% cross-instance occupancy consistency across robot pairs. An additional session recorded 6.3 ms median transform jitter and 26.7 m^2 coverage of a 41 m^2 grid. We position MR-SLAM as a reference implementation for combining passthrough mixed reality supervision with multi-robot SLAM on consumer hardware.

Prakash Aryan, Cem Erdogdu, Kavinaya Kumarchokkappan +2

University of Bern, Bern, Switzerland · AI4I – The Italian Institute of Artificial Intelligence, Turin, Italy

Simultaneous Localization and Mapping Multi-Robot Systems Robotics

May 13, 2026cs.RO

Manipulation Planning for Construction Activities with Repetitive Tasks

In this paper, we study the problem of manipulation skill acquisition for performing construction activities consisting of repetitive tasks (e.g., building a wall or installing ceiling tiles). Our approach involves setting up a simulated construction activity in a Virtual Reality (VR) environment, where the user can provide demonstrations of the object manipulation skills needed to perform the construction activity. We then exploit the screw geometry of motion to approximate the demonstrated motion as a sequence of constant screw motions. For performing the construction activity, we generate the sequence of manipulation task instances and then compute the joint space motion plan corresponding to each instance using Screw Linear Interpolation (ScLERP) and Resolved Motion Rate Control (RMRC). We evaluate our framework by executing two representative construction tasks: constructing brick walls and installing multiple ceiling tiles. Each task is performed using only a single demonstration, a pick-and-place action for the bricks, and a single ceiling tile installation. Our experiments with a 7-DoF robot in both simulation and hardware demonstrate that the approach generalizes robustly to arbitrarily long construction activities that involve repetitive motions and demand precision, even when provided with just one demonstration. For instance, we can construct walls of arbitrary layout and length by leveraging a single demonstration of placing one brick on top of another.

Wangyi Liu, Dasharadhan Mahalingam, Fanru Gao +2

Department of Mechanical Engineering, Stony Brook University, NY, USA. · Department of Civil Engineering, Stony Brook University, NY, USA.

Robotic Construction Long-Horizon Robotic Manipulation Robot Skill Learning Generalization in Robotic Manipulation Robotics Robot Task Planning+2

May 13, 2026cs.RO

TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics

Semidefinite programming (SDP) provides a principled framework for convex relaxations of nonconvex geometric constraints in motion planning, yet existing solvers are too computationally expensive for real-time control, particularly on resource-constrained embedded systems. To address this gap, we introduce TinySDP, the first semidefinite programming solver designed for embedded systems, enabling real-time model-predictive control (MPC) on microcontrollers for problems with nonconvex obstacle constraints. Our approach integrates positive-semidefinite cone projections into a cached-Riccati-based ADMM solver, leveraging computational structure for embedded tractability. We pair this solver with an a posteriori rank-1 certificate that converts relaxed solutions into explicit geometric guarantees at each timestep. On challenging benchmarks, e.g., cul-de-sac and dynamic obstacle avoidance scenarios that induce failures in local methods, TinySDP achieves collision-free navigation with up to 73% shorter paths than state-of-the-art baselines. We validate our approach on a Crazyflie quadrotor, demonstrating that semidefinite constraints can be enforced at real-time rates for agile embedded robotics.

Ishaan Mahajan, Jon Arrizabalaga, Andrea Grillo +4

Columbia University, USA · Massachusetts Institute of Technology, USA · ´Ecole Polytechnique F´ed´erale de Lausanne (EPFL), Switzerland +1

Semidefinite Programming Constrained Motion Planning Robotics Model Predictive Control Constrained Optimization Safe Motion Planning

May 13, 2026cs.RO

Object Manipulation of the Variable Topology Truss system

This paper presents an object manipulation strategy for the Variable Topology Truss (VTT) system, a truss robot that comprises actuated truss members connected by passive spherical joints. Although truss robots were originally proposed as rapidly deployable manipulators, manipulation strategy has not been studied thoroughly. To enable manipulation, we introduce a hybrid control framework that regulates position and force concurrently without explicit decoupling. At the actuator level, each member employs a sensor-based force feedback controller to generate the desired axial forces despite high actuator friction. At the task level, the forces applied at the end-effector nodes are produced by computing the required member forces using a static model of the VTT. We evaluate force-tracking performance through experiments on both a single member module and the full VTT system. Finally, we demonstrate object manipulation using two representative configurations and quantitatively assess combined position and force tracking performance. Experimental results confirm that the proposed approach enables consistent and reliable object manipulation with the VTT system.

Andrew Jang-Ho Bae, Myeongjin Choi, Haorui Li +2

Department of Mechanical Engineering University of Nevada, Las Vegas Las Vegas, NV 89154 · Advanced Robotics Research Center Research Institute of AI Robotics Korea Institute of Machinery and Materials Daejeon 34103 · RealMan Robotics Co., Ltd. Beijing, 100043 +2

Robotic Control Robotics Robotic Manipulation

May 12, 2026cs.RO

Few-Shot Physics-Informed Neural Network for Shape Reconstruction of Concentric-Tube Robots

Modeling concentric tube robots (CTRs) involves complex nonlinear continuum mechanics, and despite recent progress, physics-based models often lack an accurate representation of the experimental setups. To overcome these limitations, deep neural network-based models have been explored as alternatives with superior accuracy; however, they often overlook known mechanics, require large training datasets, and typically discard shape estimation of the robot. We present a physics-informed neural network (PINN) for kinematic modeling of a 6-DoF CTR with three pre-curved tubes that embeds the Cosserat rod differential equations and learns from few-shot observational data, balancing physics priors with data-driven fitting. PINN enables full-state estimation of shape, twist angle, torsional strain, bending moment, and orientation. Benchmark tests show a mean shape error below 1% of the robot length and accurately recovered other kinematic states, outperforming a purely physics-based Cosserat rod model baseline while using a minimal training set. The resulting model is also computationally efficient and robust, making it well-suited for real-time control applications.

Navid Feizi, Filipe C. Pedrosa, Rajni V. Patel +1

Canadian Surgical Technologies and Advanced Robotics (CSTAR), London Health Sciences Centre (LHSC), and with the Department of Electrical and Computer Engineering, Western University, London, Ontario, Canada

Robotics Physics-Informed ML Robot State Estimation

May 12, 2026cs.RO

The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy

We introduce and open-source the Unified Autonomy Stack, a system-level solution that enables resilient autonomy across diverse aerial and ground robot morphologies. The architecture centers on three synergistic modules -- multi-modal perception, multi-behavior planning, and multi-layered safe navigation -- that together deliver comprehensive mission autonomy. The stack fuses data from LiDAR, radar, vision, and inertial sensing, enabling (a) robust localization and mapping through factor graph-based fusion, (b) semantic scene understanding, (c) motion and informative path planning through sampling-based techniques adaptive across spatial scales, as well as (d) multi-layered safe navigation both through planning on the online reconstructed map and deep learning-driven exteroceptive policies alongside last-resort safety filters using control barrier functions. The resulting behaviors include safe GNSS-denied navigation into unknown and perceptually-degraded regions, exploration of complex environments, object discovery, and efficient inspection planning. The stack has been field-tested and validated on both aerial (rotorcraft) and ground (legged) robots operating in a host of demanding environments, including self-similar and smoke-filled settings, with complex geometries and high obstacle clutter. These tests demonstrate resilient performance in challenging conditions. To facilitate ease of adoption, we open-source the implementation alongside supporting documentation, validation, and evaluation datasets https://github.com/ntnu-arl/unified_autonomy_stack. A video giving the overview of the paper and the field experiments is available at https://youtu.be/l8Su8OXsM-E.

Mihir Dharmadhikari, Nikhil Khedekar, Mihir Kulkarni +7

Autonomous Robots Lab, Norwegian University of Science and Technology (NTNU), Trondheim, Norway · Department of Engineering Cybernetics, Norwegian University of Science and Technology, Høgskoleringen 1, Trondheim 7034, Norway

Robotics Safe Robot Navigation Robot Motion Planning Robotic Perception

May 12, 2026cs.RO

3D RL-DWA: A Hybrid Reinforcement Learning and Dynamic Window Approach for Goal-Directed Local Navigation in Multi-DoF Robots

In this paper, we present a novel hybrid approach that combines Reinforcement Learning (RL) with Dynamic Window Approach (DWA) for adaptive 3D local navigation of high-degree-of-freedom robotic systems. Our method leverages sparse point cloud data to dynamically adjust both the motion and the shape of a deformable microrobot, enabling the system to navigate toward a goal in complex, constrained environments while maximizing the occupied volume. We evaluate our framework in a simulated vascular network. Experimental results, based on 1080 trials, indicate that integrating RL with a DWA-based local planner significantly enhances both deformation and navigation capabilities compared to pure RL and model-based methods. In particular, the proposed autonomous controller consistently achieves high deformation and near-perfect path completion during training and maintains robust performance in unseen scenarios. These findings highlight the potential of hybrid planning strategies for efficient and adaptive 3D navigation under sparse sensory conditions.

Chiara Castellani, Enrico Turco, Domenico Prattichizzo

Humanoids and Human Centered Mechatronics Istituto Italiano di Tecnologia 16163, Genoa, Italy · Dept. of Information Engineering and Mathematics University of Siena 53100, Siena, Italy

Robotic Control Robot Navigation Robotic RL Robotics

May 12, 2026cs.RO

COSMIC: Concurrent Optimization of Structure, Material, and Integrated Control for robotic systems

Replicating and surpassing the autonomy of natural organisms remains a long-standing goal in robotics. Yet most robotic systems have their structure, materials, and control designed separately, in sharp contrast to the co-evolution in nature. This separation often leads to suboptimal designs, and we still have a limited understanding of the individual and collective contributions of these design entities. In this work, we propose a gradient-based co-design framework that simultaneously optimizes the topology, material distribution, and control policy of a truss-lattice robot. The framework embeds mixed-type topological and material variables into a continuous design space and integrates a neural network controller within a differentiable simulator, capturing their interactions and enabling efficient gradient calculation via automatic differentiation. Furthermore, we develop a constrained optimization to navigate the highly non-convex design landscape and jointly optimize all design entities. Case studies demonstrate that the proposed framework consistently discovers diverse locomotion strategies that outperform baselines obtained through separated design. The framework is also flexible to accommodate different functional requirements and boundary conditions. Using this framework, we further extract design insights that reveal the individual and collective effects of different entities on robotic performance. The proposed framework provides a computational foundation for the autonomous co-design of robotic systems, capable of reconfiguration, locomotion, and other complex autonomous behaviors.

Qinsong Guo, Liwei Wang

Dept. of Mechanical Engineering Carnegie Mellon University Pittsburgh, PA 15213

Topology Optimization Robot Morphology-Control Co-Design Robotics Robot Locomotion Hardware-Software Co-Design

May 12, 2026cs.RO

Morphologically Equivariant Flow Matching for Bimanual Mobile Manipulation

Mobile manipulation requires coordinated control of high-dimensional, bimanual robots. Imitation learning methods have been broadly used to solve these robotic tasks, yet typically ignore the bilateral morphological symmetry inherent in such systems. We argue that morphological symmetry is an underexplored but crucial inductive bias for learning in bimanual mobile manipulation: knowing how to solve a task in one configuration directly determines how to solve its mirrored counterpart. In this paper, we formalize this symmetry prior and show that it constrains optimal bimanual policies to be ambidextrous and equivariant under reflections across the robot's sagittal plane. We introduce a

\mathbb{C}_2

-equivariant flow matching policy that enforces reflective symmetry either via a regularized training loss or an equivariant velocity network. Across planar and 6-DoF mobile manipulation tasks, symmetry-informed policies consistently improve sample efficiency and achieve zero-shot generalization to mirrored configurations absent from the training distribution. We further validate this zero-shot generalization capability on a real-world manipulation task with a TIAGo++ robot. Together, our findings establish morphological symmetry as an effective, generalizable, and scalable inductive bias for ambidextrous generative policy learning.

Max Siebenborn, Daniel Ordoñez Apraez, Sophie Lueth +4

PEARL Lab, Dept. of Computer Science, TU Darmstadt, Germany · 2Istituto Italiano di Tecnologia, 3Hessian.AI, 4Robotics Institute Germany

Flow Matching Mobile Manipulation Bimanual Robotic Manipulation Generalization in Robotic Manipulation Robotics Robot Imitation Learning

May 12, 2026cs.RO

DreamAvoid: Critical-Phase Test-Time Dreaming to Avoid Failures in VLA Policies

Vision-Language-Action (VLA) models are often brittle in fine-grained manipulation, where minor action errors during the critical phases can rapidly escalate into irrecoverable failures. Since existing VLA models rely predominantly on successful demonstrations for training, they lack an explicit awareness of failure during these critical phases. To address this, we propose DreamAvoid, a critical-phase test-time dreaming framework that enables VLA models to anticipate and avoid failures. We also introduce an autonomous boundary learning paradigm to refine the system's understanding of the subtle boundary between success and failure. Specifically, we (1) utilize a Dream Trigger to determine whether the execution has entered a critical phase, (2) sample multiple candidate action chunks from the VLA via an Action Proposer, and (3) employ a Dream Evaluator, jointly trained on mixed data (success, failure, and boundary cases), to "dream" the short-horizon futures corresponding to the candidate actions, evaluate their values, and select the optimal action. We conduct extensive evaluations on real-world manipulation tasks and simulation benchmarks. The results demonstrate that DreamAvoid can effectively avoid failures, thereby improving the overall task success rate. Across four real-world tasks, DreamAvoid achieves 72.5% success, compared with 48.8% for the base policy and 54.4% for GPC-RANK. Our code is available at https://github.com/XianzheFan/DreamAvoid.

Xianzhe Fan, Yuxiang Lu, Shenyuan Gao +4

HKU · HKUST · Northwestern University

Robotics Robotic Manipulation Vision-Language-Action Models

May 12, 2026cs.RO

Rainbow Deep Q-Learning with Kinematics-Aware Design for Cooperative Delta and 3-RRS Parallel Robot Insertion

This paper presents a kinematics-aware deep reinforcement learning framework based on Rainbow Deep Q-Networks (DQN) for cooperative peg-in-hole manipulation by a Delta parallel robot and a 3-RRS (Revolute--Revolute--Spherical) parallel manipulator. A key contribution is the integration of a geometric design-optimization stage that precedes learning: the 3-RRS geometry is tuned to maximize the singularity-free workspace and improve conditioning, which in turn enlarges the safe region in which the reinforcement learning policy can explore. Together the two manipulators expose a 6~degree-of-freedom (DoF) controllable subspace (three Delta translations, two 3-RRS rotations, and one 3-RRS vertical translation); the peg-in-hole task is invariant to rotation about the peg axis, so the task-relevant manifold is five dimensional. The cooperative insertion problem is cast as a Markov Decision Process with a 12-dimensional state vector and a discrete action set containing

6 \times 2 = 12

incremental commands (one positive and one negative per controlled DoF). A shaped reward combines dense proximity guidance, penalties for kinematic and workspace violations, and sparse bonuses for successful insertions. The Rainbow DQN -- integrating double Q-learning, dueling architecture, prioritized replay, multi-step returns, noisy linear layers for exploration, and a distributional value head -- is trained with a two-stage curriculum. The co-designed framework is validated in a high-fidelity kinematic simulator, where it achieves stable policy convergence, reliable insertions, and reduced constraint violations compared against a vanilla DQN agent and a classical sampling-based planner.

Hassen Nigatu, Gaokun Shi, Jituo Li +2

Dexterous Manipulation Deep Q-Learning Robotics Robotic Insertion Robotic Manipulation

May 12, 2026cs.RO

RIO: Flexible Real-Time Robot I/O for Cross-Embodiment Robot Learning

Despite recent efforts to collect multi-task, multi-embodiment datasets, to design recipes for training Vision-Language-Action models (VLAs), and to showcase these models on different robot platforms, generalist cross-embodiment robot capabilities remains a largely elusive ideal. Progress is limited by fragmented infrastructure: most robot code is highly specific to the exact setup the user decided on, which adds major overhead when attempting to reuse, recycle, or share artifacts between users. We present RIO (Robot I/O), an open source Python framework that provides flexible, lightweight components for robot control, teleoperation, data formatting, sensor configuration, and policy deployment across diverse hardware platforms and morphologies. RIO provides abstractions that enable users to make any choice and to switch between them, with minimal reconfiguration effort. We validate RIO on VLA deployment workflows across three morphologies (single-arm, bimanual, humanoid) and four hardware platforms with varying grippers and cameras. Using teleoperated data collected with RIO, we fine-tune state-of-the-art VLAs including

π_{0.5}

and GR00T on household tasks such as pick-and-place, folding, and bowl scrubbing. By open sourcing all our efforts, we hope the community can accelerate their pace of robot learning on real-world robot hardware. Additional details at: https://robot-i-o.github.io

Pablo Ortega-Kral, Eliot Xing, Arthur Bucker +13

Carnegie Mellon University · TU Delft · Lavoro AI +1

Cross-Embodiment Robot Learning Robotics

May 11, 2026cs.RO

Computational Design of a Low-Visibility UAV Using a Human-Aligned Perceptual Metric

We introduce Phantom Twist, a type of single-propeller UAV designed to achieve low visibility through high-speed spinning and the exploitation of motion blur. We develop a two-stage automated design pipeline that optimizes the placement of functional components including batteries, control PCB, motor-propeller assembly, and counterweights. The pipeline minimizes visibility as measured by a human-aligned perceptual metric (LPIPS) while strictly satisfying inertial and aerodynamic constraints required for stable flight. We validate this approach through fabrication and flight testing of multiple prototypes. These tests confirm that our pipeline produces stable, controllable designs and that the optimized UAV exhibits significantly reduced visual perceptibility compared to conventional quadcopters.

Jingxian Wang, Chen Yu, David Matthews +3

Northwestern University, Evanston, Illinois, USA

Aerial Robotics Robotics

May 10, 2026cs.RO

SABER: A Scalable Action-Based Embodied Dataset for Real-World VLA Adaptation

Robotic deployment in real-world environments depends on rich, domain-specific action data as much as on strong model architecture. General-purpose robot foundation models show modest performance in complex unseen tasks such as manipulation in a retail domain when applied out of the box. The root cause is a data gap: retail environments are structurally absent from general robot pretraining distributions, and the path to filling that gap through teleoperation is prohibitively expensive, logistically constrained, and difficult to scale. We introduce SABER, a high-fidelity retail robotics action dataset built from over 100 hours of natural in-store capture across multiple real grocery environments. Egocentric footage from head-mounted cameras records fine-grained hand activity at the point of interaction, while exocentric 360-degree scene footage from DreamVu's ALIA camera simultaneously observes all actors and activities across the entire space. This combination yields a uniquely complete picture of human retail behavior: dexterous hand activity, whole-body motion, and scene dynamics, all captured without staging, scripting, or teleoperation overhead. The SABER corpus contains 44.8K training samples across three action representation streams: 25K latent action sequences via LAPA-style encoding, 18.6K dexterous hand-pose trajectories retargeted to robot joint space, and 1.2K whole-body synchronized motion sequences retargeted to a humanoid embodiment. When applied to GR00T N1.6 via a shared-backbone multi-task post-training recipe, SABER yields a mean success rate of 29.3% across ten retail manipulation tasks -- more than 2.19x over fine-tuning baselines (13.4%). SABER demonstrates that the path to capable retail robots runs through better data, which can be collected today, at scale, without a robot in the loop. The dataset and code are available at https://dreamvu.ai/saber

Narsimha Menga, Parikshit Sakurikar, Amirreza Rouhi +6

DreamVu1

Robot Skill Learning Robotic Data Collection Robot Foundation Models Robotics Robotic Manipulation Vision-Language-Action Models

May 10, 2026cs.RO

High Precision Hydraulic Excavator Control for Heavy-Duty Grading

High-precision heavy-duty grading is a common step in earthworks, traditionally carried out manually by skilled operators. Removing a significant amount of material while achieving a high-precision surface requires substantial machine-specific experience. Different hydraulic architectures react differently to operator inputs and soil interaction forces, which makes generalizable controllers challenging. In this paper, we present an autonomous controller that achieves high-precision grading at expert-operator speed on Load Sensing and Negative Flow Control machines alike. We split our controller into two parts: (1) a hydraulic-aware low-level loop that is hydraulic architecture-specific and (2) a path-tracking layer that coordinates joint motions and responses. Through a calibration process, our technique is applicable to load-sensing and negative-flow-control machinery. To showcase its versatility, we benchmark our approach on two excavators with different hydraulics and compare it against a commercial state-of-the-art solution. Our technique (RMSE 1.8~~cm) outperforms the commercial solution (RMSE 4.7~~cm) in precision by a factor of 2.6 and improves machine usage by leveraging the maximum function pressure, as opposed to commercial solutions that stall prematurely.

Lennart Werner, Pol Eyschen, Sean Costello +2

ETH Zürich, Robotic Systems Lab, Zürich, Switzerland · Hexagon AB, Heerbrugg, Switzerland

Robotic Control Robotics Automated Grading

May 10, 2026cs.RO

RePO-VLA: Recovery-Driven Policy Optimization for Vision-Language-Action Models

Vision-Language-Action (VLA) models remain brittle in long-horizon, contact-rich manipulation because success-only imitation provides little supervision for execution drift, while failed rollouts are often discarded. We introduce RePO-VLA, a recovery-driven policy optimization framework that assigns distinct roles to success, recovery, and failure trajectories. RePO-VLA first applies Recovery-Aware Initialization (RAI), slicing recovery segments and resetting history so corrective actions depend on the current adverse state rather than the preceding failure. It then learns a Progress-Aware Semantic Value Function (PAS-VF), aligning spatiotemporal trajectory features with instructions and successful references. The resulting labels salvage useful failure prefixes via reliability decay, while low-value labels mark drift and terminal breakdowns, teaching differences among nominal, failed, and corrective actions. The data engine turns adverse states into planner-generated or human-collected corrective rollouts, teaching recovery to the success manifold. Value-Conditioned Refinement (VCR) trains the policy to prefer high-progress actions. At deployment, a fixed high value (

v=1.0

) biases actions toward the learned success manifold without online failure detectors or heuristic retries. We introduce FRBench, with standardized error injection and recovery-focused evaluation. Across simulated and real-world bimanual tasks, RePO-VLA improves robustness, raising adversarial success from 20% to 75% on average and up to 80% in scaled real-world trials.

Weijia Liufu, Xiaoyu Guo, Ruiyi Chen +16

1Sun Yat-sen University · 2South China University of Technology · 3Peng Cheng Laboratory +3

VLM Robustness Robot Failure Recovery Robotics Vision-Language-Action Models Robustness of VLA Models

May 9, 2026cs.RO

HyDRA Scorpion: A Cost-effective and Modular ROV for Real-Time Underwater Inspection, Intervention, and Object Detection

A Remotely Operated Vehicle (ROV) is a tethered underwater robot used for tasks like inspection and intervention. While essential tools for underwater science, the high cost of commercial ROVs and a persistent gap between mechanically capable platforms and those with integrated intelligence create a significant barrier to access. HyDRA Scorpion differs from conventional systems by addressing these challenges, integrating an advanced, AI-driven perception stack with in-situ measurement capabilities onto a low-cost, locally manufacturable platform. The system combines 4-DoF maneuverability, dual manipulators, and a custom pressure-tested housing. Experimental results validate the system's robustness and performance. Leak-free operation was confirmed through prolonged pressure testing of the electronics housing to 4 bar, equivalent to the pressure of a 304.8-meter water depth approximately in a simulated environment, with no moisture ingress detected. The vehicle also demonstrated stable station-keeping, maintaining its position within a tight tolerance of $$\pm

0.15

meters under external disturbances. The onboard AI module achieved underwater object detection mean Average Precision (mAP) of 0.89 with real-time inference, length and 3D-mapping based distance measurement. Also, 4-DoF manipulator arm can grip and maintain dual-function manipulator feature which support 360 degree tangle-free rotation.

Anika Tabassum Orchi, Md Farhan Zaman, Md Darain Khan +16

Department of Computer Science and Engineering, United International University, United City, Madani Ave, Dhaka 1212, Bangladesh · Department of Electrical and Electronic Engineering, United International University, United City, Madani Ave, Dhaka 1212, Bangladesh

Robotics Robotic Manipulation Underwater Robotics Real-Time Object Detection Underwater Object Detection

May 9, 2026cs.RO

Octopus Protocol: One-Shot Hardware Discovery and Control for AI Agents via Infrastructure-as-Prompts

Recent agentic-robotics systems, from Code-asPolicies to modern vision-language-action (VLA) foundation models, presuppose that drivers, SDKs, or ROS-style primitives for the target hardware already exist. Writing those primitives is the dominant engineering cost of bringing up new hardware for agent control. We present Octopus Protocol, a system that collapses that cost to a single shell command. Given only raw OS access and a language-model API key, a coding agent executes a five-stage pipeline--PROBE, IDENTIFY, INTERFACE, SERVE, DEPLOY--to discover connected devices, infer their capabilities, generate a Model Context Protocol (MCP) server with typed tools, and deploy it as a live HTTP endpoint. A persistent daemon then monitors the system, heals broken code, and perceives physical state through the camera tools it generated for itself. Two architectural principles make this work: protocols are prompts, not code, and the coding agent is the runtime. We validate the system on three heterogeneous platforms (PC/WSL, Apple Silicon macOS, Raspberry Pi 4) and on a commercial 6-DOF robotic arm with USB camera feedback. One command onboards the hardware in ~10-15 minutes and exposes up to 30 MCP tools; an MCP-compliant client then performs closed-loop visual-motor control through tools no human wrote.

Quilee Simeon, Justin M. Wei, Yile Fan

MIT · Harvard

Robotics AI Control Tool-Using Agents

May 9, 2026cs.RO

Omni-scale Learning-based Sequential Decision Framework for Order Fulfillment of Tote-handling Robotic Systems

Driven by the rapid expansion of e-commerce and small-batch production, the size of the intralogistics load unit of finished goods, semi-finished goods and raw materials is steadily shrinking. Totes are gradually replacing pallets as the primary handling and storage container. This shift has propelled tote-handling robotic systems to the forefront of automation order fulfillment centers. The order-fulfillment decisions of tote-handling robotic systems share a common order-tote-robot sequential decision-making nature. Existing studies primarily focus on decision mechanisms tailored to particular systems, making it difficult to generalize or transfer them to other contexts. We propose an Omni-scale Learning-based Sequential Decision Framework for Order Fulfillment of Tote-handling Robotic Systems (OLSF-TRS), a generalized and scalable sequential decision framework that combines structured combinatorial optimization with multi-agent reinforcement learning to coordinate order,tote, and robot decisions. On small-scale tote-handling robotic systems, OLSF-TRS achieves near-optimal performance with average optimality gaps below 3.5% across two distinct system configurations. In large-scale scenarios, OLSF-TRS consistently outperforms heuristic baselines across two different system types, reducing total tote movements by 8-12% and over 30% compared to SOTA rule-based approaches, while maintaining real-time responsiveness. These improvements translate into tangible operational benefits, including cost reduction, lower energy consumption, and enhanced throughput stability. The proposed framework delivers an efficient and unified order fulfillment decision-making framework for widely deployed tote-handling robotic systems,supporting high-quality order fulfillment in both e-commerce and industrial logistics sectors.

Jiaxin Liu, Peng Yang, Yuping Li +1

Institution of Data and Information, Shenzhen International Graduate School, Tsinghua University, Nanshan District, Shenzhen 518055, China

Combinatorial Optimization Robotics Multi-Robot Coordination Multi-Robot Task Allocation

May 8, 2026cs.AI

FactoryBench: Evaluating Industrial Machine Understanding

We introduce FactoryBench, a benchmark for evaluating time-series models and LLMs on machine understanding over industrial robotic telemetry. Q&A pairs are organized along four causal levels (state, intervention, counterfactual, decision) instantiating Pearl's ladder of causation, and span five answer formats: four structured formats are scored deterministically and free-form answers are scored by an LLM-as-judge voting protocol. We propose a scalable Q&A generation framework built around structured question templates, present FactoryWave (a dense, multitask, multivariate sensor dataset collected from a UR3 cobot and a KUKA KR10 industrial arm), and construct FactoryBench as a large-scale benchmark of over 70k Q&A items grounded in roughly 15k normalized episodes from FactoryWave, AURSAD, and voraus-AD. Zero-shot evaluation of six frontier LLMs shows that no model exceeds 50% on structured levels or 18% on decision-making, revealing a wide gap between current models and operational machine understanding.

Yanis Merzouki, Coral Izquierdo, Matei Ignuta-Ciuncanu +8

1ETH Zurich · 2Forgis · 3UC3M +4

Manufacturing LLM Evaluation Robotics Causal Reasoning Industrial Robotics

May 7, 2026cs.RO

Lie Group Formulation of Recursive Dynamics Algorithms of Higher Order for Floating-Base Robots

In this paper, we describe procedures for computing higher-order time derivatives of the Lie-group Newton-Euler, Articulated-Body Inertia, and hybrid dynamics algorithms for floating-base trees, where the base configuration evolves on SE(3) and the attached mechanism is an open kinematic tree with configuration on the (n1+n2)-dimensional manifold T^{n1} \times R^{n2}, using spatial representation of twists. After presenting the algorithms, we collect the resulting recursions into closed-form equations of motion, identifying an admissible Coriolis matrix satisfying the passivity property, and showing that the articulated inertia tensor remains unchanged across all time derivatives. We then apply the developed methods to a 12-DoF aerial manipulator to derive analytical expressions for its geometric forward and inverse dynamics along with their first time derivatives whereas the numerical simulations successfully evaluate these dynamics up to fifth order. Finally, to demonstrate their practical utility, we benchmark the proposed extensions and show that, in the considered tests, their computational cost scales quadratically with the derivative order, whereas the automatic-differentiation baseline exhibits exponential scaling.

Ahmed Ali, Chiara Gabellieri, Antonio Franchi

Robotics and Mechatronics Department, Electrical Engineering, Mathematics, and Computer Science (EEMCS) Faculty, University of Twente, 7500 AE Enschede, The Netherlands. · Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy.

Multibody Dynamics Robotics Lie Group Methods Inverse Dynamics