Robotics

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Robotics brings together mechanical design, sensing, perception, planning, and control so machines can act in changing physical environments, from homes and factories to fields, hospitals, and space. Approaches range from geometric models, feedback and model-predictive control, and motion planning to reinforcement learning, imitation learning, and vision-language-action models; many systems combine these with specialized hardware such as soft, aerial, legged, or continuum robots. Simulation, shared datasets, and benchmarks help compare methods, while real-robot experiments test whether they transfer across embodiments, conditions, and long tasks. A central challenge is making learned systems reliable, safe, and efficient outside controlled demonstrations, where uncertainty, contact, hardware limits, and human needs all matter.

Latest papers

755 papers77 papers in the last four weeks, down 33% on the four weeks before. 0.4% of all new papers.

Mar 13, 2026

Creating manufacturable blueprints for coarse-grained virtual robots

Over the past three decades, countless embodied yet virtual agents have freely evolved inside computer simulations, but vanishingly few were realized as physical robots. This is because evolution was conducted at a level of abstraction that was convenient for freeform body generation (creation, mutation, recombination) but swept away almost all of the physical details of functional body parts. The resulting designs were crude and underdetermined, requiring considerable effort and expertise to convert into a manufacturable format. Here, we automate this mapping from simplified design spaces that are readily evolvable to complete blueprints that can be directly followed by a builder. The pipeline incrementally resolves manufacturing constraints by embedding the structural and functional semantics of motors, electronics, batteries, and wiring into the abstract virtual design. In lieu of evolution, a user-defined or AI-generated ``sketch'' of a body plan can also be fed as input to the pipeline, providing a versatile framework for accelerating the design of novel robots.

Zihan Guo, Muhan Li, Shuzhe Zhang +1

Northwestern University

Robotics Evolutionary Robotics

Mar 13, 2026

Safety-aware Goal-oriented Semantic Sensing, Communication, and Control for Robotics

Wirelessly-connected robotic systems empower robots with real-time intelligence by leveraging remote computing resources for decision-making. However, the data exchange between robots and edge servers often overwhelms communication links, introducing latency that degrades task performance. To tackle this, goal-oriented semantic communication (GSC) has been introduced for wirelessly-connected robotic systems to extract and transmit only goal-relevant semantic representations. While this improves task effectiveness, it generally overlooks practical safety requirements. Meanwhile, existing robotics research often treats safety primarily as a control-level problem, without systematically considering safety across sensing, communication, and control in a closed-loop manner. To bridge this gap, we investigate how to enable safety-aware goal-oriented semantic (SA-GS) sensing, communication, and control co-design in wirelessly-connected robotic systems, aiming to maximize the robotic task effectiveness subject to practical safety requirements. We first introduce {an} architecture {for} wirelessly-connected robotic systems and representative use cases. We then summarize general safety requirements and effectiveness metrics across the use cases. Next, we systematically analyze the unique safety and effectiveness challenges in sensing, communication, and control. Based on these, we further present potential SA-GS research directions. Finally, an Unmanned Aerial Vehicle (UAV) target tracking case study validates that one of the presented SA-GS research directions, i.e., semantic-based C&C packet execution, could significantly improve safety rate and tracking success rate by more than 2 times and 4.5 times, respectively.

Wenchao Wu, Shutong Chen, Wenjie Liu +3

Department of Engineering, King’s College London, U.K. · Department of Intelligent Systems, KTH Royal Institute of Technology, 10044 Stockholm, Sweden · Department of Automation Technology, ABB Corporate Research Sweden, 72226 Västerås, Sweden +1

Robotics Robot Safety Task-Oriented Semantic Communication Integrated Sensing and Communication

Mar 13, 2026

Learning Athletic Humanoid Tennis Skills from Imperfect Human Motion Data

Human athletes demonstrate versatile and highly-dynamic tennis skills to successfully conduct competitive rallies with a high-speed tennis ball. However, reproducing such behaviors on humanoid robots is difficult, partially due to the lack of perfect humanoid action data or human kinematic motion data in tennis scenarios as reference. In this work, we propose LATENT, a system that Learns Athletic humanoid TEnnis skills from imperfect human motioN daTa. The imperfect human motion data consist only of motion fragments that capture the primitive skills used when playing tennis rather than precise and complete human-tennis motion sequences from real-world tennis matches, thereby significantly reducing the difficulty of data collection. Our key insight is that, despite being imperfect, such quasi-realistic data still provide priors about human primitive skills in tennis scenarios. With further correction and composition, we learn a humanoid policy that can consistently strike incoming balls under a wide range of conditions and return them to target locations, while preserving natural motion styles. We also propose a series of designs for robust sim-to-real transfer and deploy our policy on the Unitree G1 humanoid robot. Our method achieves surprising results in the real world and can stably sustain multi-shot rallies with human players. Project page: https://zzk273.github.io/LATENT/

Zhikai Zhang, Haofei Lu, Yunrui Lian +12

Tsinghua University · Galbot Inc. · Shanghai AI Laboratory +2

Robotics Humanoid Robot Control Robot Imitation Learning

Mar 12, 2026

Robots that redesign themselves through kinematic self-destruction

Every robot built to date was predesigned by an external process, prior to deployment. Here we show a robot that actively participates in its own design during its lifetime. Starting from a randomly assembled body, and using only proprioceptive feedback, the robot dynamically ``sculpts'' itself into a new design through kinematic self-destruction: identifying redundant links within its body that inhibit its locomotion, and then thrashing those links against the surface until they break at the joint and fall off the body. It does so using a single autoregressive sequence model, a universal controller that learns in simulation when and how to simplify a robot's body through self-destruction and then adaptively controls the reduced morphology. The optimized policy successfully transfers to reality and generalizes to previously unseen kinematic trees, generating forward locomotion that is more effective than otherwise equivalent policies that randomly remove links or cannot remove any. This suggests that self-designing robots may be more successful than predesigned robots in some cases, and that kinematic self-destruction, though reductive and irreversible, could provide a general adaptive strategy for a wide range of robots.

Chen Yu, Sam Kriegman

Northwestern University

Robotics Robot Morphology-Control Co-Design Robot Learning Robot Locomotion

Mar 12, 2026

A Learning-Based Approach for Contact Detection, Localization, and Force Estimation of Continuum Manipulators With Integrated OFDR Optical Fiber

Continuum manipulators (CMs) are widely used in minimally invasive procedures due to their compliant structure and ability to navigate deep and confined anatomical environments. However, their distributed deformation makes force sensing, contact detection, localization, and force estimation challenging, particularly when interactions occur at unknown arc-length locations along the robot. To address this problem, we propose a cascade learning-based framework (CLF) for CMs instrumented with a single distributed Optical Frequency Domain Reflectometry (OFDR) fiber embedded along one side of the robot. The OFDR sensor provides dense strain measurements along the manipulator backbone, capturing strain perturbations caused by external interactions. The proposed CLF first detects contact using a Gradient Boosting classifier and then estimates contact location and interaction force magnitude using a CNN--FiLM model that predicts a spatial force distribution along the manipulator. Experimental validation on a sensorized tendon-driven CM in an obstructed environment demonstrates that a single distributed OFDR fiber provides sufficient information to jointly infer contact occurrence, location, and force in continuum manipulators.

Mobina Tavangarifard, Jonathan S. Kacines, Qiyu Li +1

Walker Department of Mechanical Engineering and Texas Robotics, The University of Texas at Austin, Austin, TX 78712, USA. · Department of Computer Science, The University of Texas at Austin, Austin, TX 78712, USA.

Mar 12, 2026

HomeSafe-Bench: Evaluating Vision-Language Models on Unsafe Action Detection for Embodied Agents in Household Scenarios

The rapid evolution of embodied agents has accelerated the deployment of household robots in real-world environments. However, unlike structured industrial settings, household spaces introduce unpredictable safety risks, where system limitations such as perception latency and lack of common sense knowledge can lead to dangerous errors. Current safety evaluations, often restricted to static images, text, or general hazards, fail to adequately benchmark dynamic unsafe action detection in these specific contexts. To bridge this gap, we introduce HomeSafe-Bench, a challenging benchmark designed to evaluate Vision-Language Models (VLMs) on unsafe action detection in household scenarios. HomeSafe-Bench is contrusted via a hybrid pipeline combining physical simulation with advanced video generation and features 438 diverse cases across six functional areas with fine-grained multidimensional annotations. Beyond benchmarking, we propose Hierarchical Dual-Brain Guard for Household Safety (HD-Guard), a hierarchical streaming architecture for real-time safety monitoring. HD-Guard coordinates a lightweight FastBrain for continuous high-frequency screening with an asynchronous large-scale SlowBrain for deep multimodal reasoning, effectively balancing inference efficiency with detection accuracy. Evaluations demonstrate that HD-Guard achieves a superior trade-off between latency and performance, while our analysis identifies critical bottlenecks in current VLM-based safety detection.

Jiayue Pu, Zhongxiang Sun, Zilu Zhang +2

Gaoling School of Artificial Intelligence, Renmin University of China · University of Chinese Academy of Sciences · Beijing University of Posts and Telecommunications

Cultural Safety Evaluation of VLMs AI Agent Safety AI Agent Safety Benchmarks VLM Evaluation Robotics Efficient Multimodal Inference

Mar 12, 2026

From Pets to Robots: MojiKit as a Data-Informed Toolkit for Affective HRI Design

Designing affective behaviors for animal-inspired social robots often relies on intuition and personal experience, leading to fragmented outcomes. To provide more systematic guidance, we first coded and analyzed human-pet interaction videos, validated insights through literature and interviews, and created structured reference cards that map the design space of pet-inspired affective interactions. Building on this, we developed MojiKit, a toolkit combining reference cards, a zoomorphic robot prototype (MomoBot), and a behavior control studio. We evaluated MojiKit in co-creation workshops with 18 participants, finding that MojiKit helped them design 35 affective interaction patterns beyond their own pet experiences, while the code-free studio lowered the technical barrier and enhanced creative agency. Our contributions include the data-informed structured resource for pet-inspired affective HRI design, an integrated toolkit that bridges reference materials with hands-on prototyping, and empirical evidence showing how MojiKit empowers users to systematically create richer, more diverse affective robot behaviors.

Liwen He, Pingting Chen, Ziheng Tang +4

The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China · Tsinghua University, Beijing, China · Beijing Institute of Technology, Beijing, China +3

Human-Robot Interaction Social Robotics Robotics

Mar 12, 2026

MiNI-Q: A Miniature, Wire-Free Quadruped with Unbounded, Independently Actuated Leg Joints

Physical joint limits are common in legged robots and can restrict workspace, constrain gait design, and increase the risk of hardware damage. This paper introduces MiNI-Q^2, a miniature, wire-free quadruped robot with independently actuated, mechanically unbounded 2-DOF leg joints. We present the mechanical design, kinematic analysis, and experimental validation of the proposed robot. The leg mechanism enables both oscillatory gaits and rotary locomotion while allowing the robot to fold to a minimum height of 2.5 cm. Experimentally, MiNI-Q achieves speeds up to 0.46 m/s and demonstrates low-clearance crawling, stair climbing, inverted locomotion, jumping, and backflipping. The wire-free architecture extends our previous Q8bot design, improving assembly reliability at miniature scale. All mechanical and electrical design files are released open source to support reproducibility and further research.

Daniel Koh, Suraj Shah, Yufeng Wu +1

Department of Mechanical and Aerospace Engineering, UCLA, Los Angeles, CA 90095, USA.

Legged Robotics Robotics Quadruped Locomotion

Mar 12, 2026

Deployment-Time Reliability of Learned Robot Policies

Recent advances in learning-based robot manipulation have produced policies with remarkable capabilities. Yet, reliability at deployment remains a fundamental barrier to real-world use, where distribution shift, compounding errors, and complex task dependencies collectively undermine system performance. This dissertation investigates how the reliability of learned robot policies can be improved at deployment time through mechanisms that operate around them. We develop three complementary classes of deployment-time mechanisms. First, we introduce runtime monitoring methods that detect impending failures by identifying inconsistencies in closed-loop policy behavior and deviations in task progress, without requiring failure data or task-specific supervision. Second, we propose a data-centric framework for policy interpretability that traces deployment-time successes and failures to influential training demonstrations using influence functions, enabling principled diagnosis and dataset curation. Third, we address reliable long-horizon task execution by formulating policy coordination as the problem of estimating and maximizing the success probability of behavior sequences, and we extend this formulation to open-ended, language-specified tasks through feasibility-aware task planning. By centering on core challenges of deployment, these contributions advance practical foundations for the reliable, real-world use of learned robot policies. Continued progress on these foundations will be essential for enabling trustworthy and scalable robot autonomy in the future.

Christopher Agia

THE DEPARTMENT OF COMPUTER SCIENCE AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY

Robotics Robotic Policy Evaluation Long-Horizon Robotic Manipulation Robot Task Planning

Mar 11, 2026

Novelty Adaptation Through Hybrid Large Language Model (LLM)-Symbolic Planning and LLM-guided Reinforcement Learning

In dynamic open-world environments, autonomous agents often encounter novelties that hinder their ability to find plans to achieve their goals. Specifically, traditional symbolic planners fail to generate plans when the robot's planning domain lacks the operators that enable it to interact appropriately with novel objects in the environment. We propose a neuro-symbolic architecture that integrates symbolic planning, reinforcement learning, and a large language model (LLM) to learn how to handle novel objects. In particular, we leverage the common sense reasoning capability of the LLM to identify missing operators, generate plans with the symbolic AI planner, and write reward functions to guide the reinforcement learning agent in learning control policies for newly identified operators. Our method outperforms the state-of-the-art methods in operator discovery as well as operator learning in continuous robotic domains.Our webpage and code can be access here: https://helenlu66.github.io/hybridLLMguided/

Hong Lu, Pierrick Lorang, Timothy R. Duggan +2

Department of Computer Science, Tufts University, Medford, MA, USA · Austrian Institute of Technology GmbH, Vienna, Austria

Robotics Neuro-Symbolic Reasoning Robot Learning LLM-Guided RL Symbolic Planning Neuro-Symbolic Planning+1

Mar 11, 2026

AdaClearGrasp: Learning Adaptive Clearing for Zero-Shot Robust Dexterous Grasping in Densely Cluttered Environments

In densely cluttered environments, physical interference, visual occlusions, and unstable contacts often cause direct dexterous grasping to fail, while aggressive singulation strategies may compromise safety. Enabling robots to adaptively decide whether to clear surrounding objects or directly grasp the target is therefore crucial for robust manipulation. We propose AdaClearGrasp, a closed-loop decision-execution framework for adaptive clearing and zero-shot dexterous grasping in densely cluttered environments. The framework formulates manipulation as a controllable high-level decision process that determines whether to directly grasp the target or first clear surrounding objects. A pretrained vision-language model (VLM) interprets visual observations and language task descriptions to reason about grasp interference and generate a high-level planning skeleton, which invokes structured atomic skills through a unified action interface. For dexterous grasping, we train a reinforcement learning policy with a relative hand-object distance representation, enabling zero-shot generalization across diverse object geometries and physical properties. During execution, visual feedback monitors outcomes and triggers replanning upon failures, forming a closed-loop correction mechanism. To evaluate language-conditioned dexterous grasping in clutter, we introduce Clutter-Bench, the first simulation benchmark with graded clutter complexity. It includes seven target objects across three clutter levels, yielding 210 task scenarios. We further perform sim-to-real experiments on three objects under three clutter levels (18 scenarios). Results demonstrate that AdaClearGrasp significantly improves grasp success rates in densely cluttered environments. For more videos and code, please visit our project website: https://chenzixuan99.github.io/adaclear-grasp.github.io/.

Zixuan Chen, Wenquan Zhang, Jing Fang +7

Nanjing University · Nanjing University of Posts and Telecommunications · National University of Singapore

Robotic Grasping Robotics Zero-Shot Robotic Manipulation RL for Robotics Robot Manipulation Benchmarks

Mar 11, 2026

BinWalker: Development and Field Evaluation of a Quadruped Manipulator Platform for Sustainable Litter Collection

Litter pollution represents a growing environmental problem affecting natural and urban ecosystems worldwide. Waste discarded in public spaces often accumulates in areas that are difficult to access, such as uneven terrains, coastal environments, parks, and roadside vegetation. Over time, these materials degrade and release harmful substances, including toxic chemicals and microplastics, which can contaminate soil and water and pose serious threats to wildlife and human health. Despite increasing awareness of the problem, litter collection is still largely performed manually by human operators, making large-scale cleanup operations labor-intensive, time-consuming, and costly. Robotic solutions have the potential to support and partially automate environmental cleanup tasks. In this work, we present a quadruped robotic system designed for autonomous litter collection in challenging outdoor scenarios. The robot combines the mobility advantages of legged locomotion with a manipulation system consisting of a robotic arm and an onboard litter container. This configuration enables the robot to detect, grasp, and store litter items while navigating through uneven terrains. The proposed system aims to demonstrate the feasibility of integrating perception, locomotion, and manipulation on a legged robotic platform for environmental cleanup tasks. Experimental evaluations conducted in outdoor scenarios highlight the effectiveness of the approach and its potential for assisting large-scale litter removal operations in environments that are difficult to reach with traditional robotic platforms. The code associated with this work can be found at: https://github.com/iit-DLSLab/trash-collection-isaaclab.

Giulio Turrisi, Angelo Bratta, Giovanni Minelli +4

Dynamic Legged Systems · Istituto Italiano di Tecnologia (IIT) · Genova, Italy

Robotics Robotic Manipulation

Mar 10, 2026

Receptogenesis in a Vascularized Robotic Embodiment

Equipping robotic systems with the capacity to generate

\textit{ex novo}

hardware during operation extends physical adaptability. Unlike modular systems that rely on discrete component integration pre- or post-deployment, we envision physical adaptation through continuous in-body development via hardware synthesis. Drawing inspiration from circulatory systems that redistribute mass and function in biological organisms, we utilize fluidics to restructure the material interface, a capability currently unmatched in robotics. Here, we realize this proof-of-concept hardware generation through a vascularized robotic composite designed for programmable material synthesis, demonstrated via receptogenesis - the on-demand construction of sensors. By coordinating the fluidic transport of precursors with external localized UV irradiation, we drove an

\textit{in situ}

photopolymerization that chemically reconstructed the vasculature from the inside out. This reaction converted precursors with photolatent initiator into a solid dispersion of UV-sensitive polypyrrole in PETG, establishing a sensing modality validated by a characteristic decrease in electrical impedance. The newly synthesized sensor closed a local control loop in real time to regulate wing flapping in a moth-inspired robotic demonstrator. Our work is a proof-of-concept materials basis for

\textit{ex novo}

hardware generation in a vascularized composite - a step towards situated robots adapting to environmental cues.

Kadri-Ann Pankratov, Leonid Zinatullin, Hans Priks +6

Institute of Technology, University of Tartu, Nooruse 1, 50411 Tartu, Estonia · Institute of Technology, Via Morego, 30, 16163 Genova, Italy

Robotics Soft Robotics

Mar 10, 2026

Embodied Human Simulation for Quantitative Design and Analysis of Interactive Robotics

Physical interactive robotics, ranging from wearable devices to collaborative humanoid robots, require close coordination between mechanical design and control. However, evaluating interactive dynamics is challenging due to complex human biomechanics and motor responses. Traditional experiments rely on indirect metrics without measuring human internal states, such as muscle forces or joint loads. To address this issue, we develop a scalable simulation-based framework for the quantitative analysis of physical human-robot interaction. At its core is a full-body musculoskeletal model serving as a predictive surrogate for the human dynamical system. Driven by a reinforcement learning controller, it generates adaptive, physiologically grounded motor behaviors. We employ a sequential training pipeline where the pre-trained human motion control policy acts as a consistent evaluator, making large-scale design space exploration computationally tractable. By simulating the coupled human-robot system, the framework provides access to internal biomechanical metrics, offering a systematic way to concurrently co-optimize a robot's structural parameters and control policy. We demonstrate its capability in optimizing human-exoskeleton interactions, showing improved joint alignment and reduced contact forces. This work establishes embodied human simulation as a scalable paradigm for interactive robotics design.

Chenhui Zuo, Jinhao Xu, Michael Qian Vergnolle +1

The authors are with the School of Aerospace Engineering, Tsinghua University, Beijing, China 100084.

Musculoskeletal Modeling Robotics Human-Robot Interaction Robotics Simulation Robot Morphology-Control Co-Design

Mar 10, 2026

Cutting the Cord: System Architecture for Low-Cost, GPU-Accelerated Bimanual Mobile Manipulation

We present a bimanual mobile manipulator built on the open-source XLeRobot with integrated onboard compute for less than $1300. Key contributions include: (1) optimized mechanical design maximizing stiffness-to-weight ratio, (2) a Tri-Bus power topology isolating compute from motor-induced voltage transients, and (3) embedded autonomy using NVIDIA Jetson Orin Nano for untethered operation. The platform enables teleoperation, autonomous SLAM navigation, and vision-based manipulation without external dependencies, providing a low-cost alternative for research and education in robotics and robot learning.

Artemis Shaw, Chen Liu, Justin Costa +5

Robotics Mobile Manipulation Bimanual Robotic Manipulation

Mar 9, 2026

Fly, Track, Land: Infrastructure-less Magnetic Localization for Heterogeneous UAV-UGV Teaming

Persistent air ground robot teams require nano UAVs that can leave a mobile UGV, perform short scouting or inspection tasks, and reliably return to a compact landing interface. This final docking phase remains difficult: it demands centimeter-scale relative localization on a moving platform, while nano UAVs provide only a few grams of payload and severe onboard sensing, power, and computation constraints. We address this problem with an infrastructure-less magneto inductive anchor-tag localization system for heterogeneous UAV-UGV teaming. Unlike passive magnetic docking aids or approaches exploiting environmental magnetic features, the proposed system actively generates a frequency multiplexed AC magnetic field on the UGV and uses it as an onboard localization reference for closed loop flight. The nano UAV carries only a lightweight passive receive coil and estimates its 3D position directly in the UGV frame, providing a local close range reference for hovering, tracking, and landing without external anchors, visual fiducials, GNSS, or motion capture online. The magnetic estimate is fused with the native onboard sensing stack, enabling operation under nano UAV SWaP constraints. Experiments with a Unitree A1 quadruped and a Crazyflie nano UAV, evaluated against motion capture ground truth, show centimeter level accuracy in static hovering and landing and approximately 8-11 cm RMSE during UGV motion, while the flow only baseline frequently violates the safety bound and fails the task.

Valerio Brunacci, Davide Plozza, Alessio De Angelis +2

Department of Engineering, University of Perugia, Perugia, 06125, Italy · Multi-Robot Systems Group, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 16627, Czech Republic · Department of Information Technology and Electrical Engineering (D-ITET), ETH Zurich, Zurich, 8092, Switzerland

Robotics Wireless Localization Multi-Robot Systems Cooperative Localization

Mar 9, 2026

Proprioceptive Safe Active Navigation and Exploration for Planetary Environments

Deformable granular terrains introduce significant locomotion and immobilization risks in planetary exploration and are difficult to detect via remote sensing (e.g., vision). Legged robots can sense terrain properties through leg-terrain interactions during locomotion, offering a direct means to assess traversability in deformable environments. How to systematically exploit this interaction-derived information for navigation planning, however, remains underexplored. We address this gap by presenting PSANE, a Proprioceptive Safe Active Navigation and Exploration framework that leverages leg-terrain interaction measurements for safe navigation and exploration in unknown deformable environments. PSANE learns a traversability model via Gaussian Process regression to estimate and certify safe regions and identify exploration frontiers online, and integrates these estimates with a reactive controller for real-time navigation. Frontier selection is formulated as a multi-objective optimization that balances safe-set expansion probability and goal-directed cost, with subgoals selected via scalarization over the Pareto-optimal frontier set. PSANE safely explores unknown granular terrain and reaches specified goals using only proprioceptively estimated traversability, while achieving performance improvements over baseline methods.

Matthew Y. Jiang, Feifei Qian, Shipeng Liu

Georgia Institute of Technology, Atlanta, GA 30332, USA · University of Southern California, Los Angeles, CA 90089, USA

Planetary Rover Robotics Autonomous Robot Exploration Robotics Safe Robot Navigation Mobile Robotics Terrain-Aware Robot Locomotion

Mar 9, 2026

Predictive Control with Indirect Adaptive Laws for Payload Transportation by Quadrupedal Robots

This paper formally develops a novel hierarchical planning and control framework for robust payload transportation by quadrupedal robots, integrating a model predictive control (MPC) algorithm with a gradient-descent-based adaptive updating law. At the framework's high level, an indirect adaptive law estimates the unknown parameters of the reduced-order (template) locomotion model under varying payloads. These estimated parameters feed into an MPC algorithm for real-time trajectory planning, incorporating a convex stability criterion within the MPC constraints to ensure the stability of the template model's estimation error. The optimal reduced-order trajectories generated by the high-level adaptive MPC (AMPC) are then passed to a low-level nonlinear whole-body controller (WBC) for tracking. Extensive numerical investigations validate the framework's capabilities, showcasing the robot's proficiency in transporting unmodeled, unknown static payloads up to 109% in experiments on flat terrains and 91% on rough experimental terrains. The robot also successfully manages dynamic payloads with 73% of its mass on rough terrains. Performance comparisons with a normal MPC and an L1 MPC indicate a significant improvement. Furthermore, comprehensive hardware experiments conducted in indoor and outdoor environments confirm the method's efficacy on rough terrains despite uncertainties such as payload variations, push disturbances, and obstacles.

Leila Amanzadeh, Taizoon Chunawala, Randall T. Fawcett +2

Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061, USA · Exponent Inc., Phoenix, AZ 85027, USA

Adaptive MPC Robotics Model Predictive Control Quadruped Locomotion

Mar 9, 2026

HMR-1: Hierarchical Massage Robot with Vision-Language-Model for Embodied Healthcare

The rapid advancement of Embodied Intelligence has opened transformative opportunities in healthcare, particularly in physical therapy and rehabilitation. However, critical challenges remain in developing robust embodied healthcare solutions, such as the lack of standardized evaluation benchmarks and the scarcity of open-source multimodal acupoint massage datasets. To address these gaps, we construct MedMassage-12K - a multimodal dataset containing 12,190 images with 174,177 QA pairs, covering diverse lighting conditions and backgrounds. Furthermore, we propose a hierarchical embodied massage framework, which includes a high-level acupoint grounding module and a low-level control module. The high-level acupoint grounding module uses multimodal large language models to understand human language and identify acupoint locations, while the low-level control module provides the planned trajectory. Based on this, we evaluate existing MLLMs and establish a benchmark for embodied massage tasks. Additionally, we fine-tune the Qwen-VL model, demonstrating the framework's effectiveness. Physical experiments further confirm the practical applicability of the framework.Our dataset and code are publicly available at https://github.com/Xiaofeng-Han-Res/HMR-1.

Rongtao Xu, Mingming Yu, Xiaofeng Han +7

Spatiotemporal AI, China · Hangzhou International Innovation Institute, Beihang University, China · The State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, China +2

Healthcare Robotics Robotics VLMs for Robotics Robot Manipulation Benchmarks

Mar 9, 2026

Diff-Muscle: Efficient Learning for Musculoskeletal Robotic Table Tennis

Musculoskeletal robots provide superior advantages in flexibility and dexterity, positioning them as a promising frontier towards embodied intelligence. However, current research is largely confined to relative simple tasks, restricting the exploration of their full potential in multi-segment coordination. Furthermore, efficient learning remains a challenge, primarily due to the high-dimensional action space and inherent overactuated structures. To address these challenges, we propose Diff-Muscle, a musculoskeletal robot control algorithm that leverages differential flatness to reformulate policy learning from the redundant muscle-activation space into a significantly lower-dimensional joint space. Furthermore, we utilize the highly dynamic robotic table tennis task to evaluate our algorithm. Specifically, we propose a hierarchical reinforcement learning framework that integrates a Kinematics-based Muscle Actuation Controller (K-MAC) with high-level trajectory planning, enabling a musculoskeletal robot to perform dexterous and precise rallies. Experimental results demonstrate that Diff-Muscle significantly outperforms state-of-the-art baselines in success rates while maintaining minimal muscle activation. Notably, the proposed framework successfully enables the musculoskeletal robots to achieve continuous rallies in a challenging dual-robot setting.

Wentao Zhao, Jun Guo, Kangyao Huang +2

Department of Computer Science and Technology, Tsinghua University, China.

Robotic Table Tennis Differential Flatness Robotics Robotic Control

Mar 9, 2026

The Neural Compass: Probabilistic Relative Feature Fields for Robotic Search

Object co-occurrences provide a key cue for finding objects successfully and efficiently in unfamiliar environments. Typically, one looks for cups in kitchens and views fridges as evidence of being in a kitchen. Such priors have also been exploited in artificial agents, but they are typically learned from explicitly labeled data or queried from language models. It is still unclear whether these relations can be learned implicitly from unlabeled observations alone. In this work, we address this problem and propose ProReFF, a feature field model trained to predict relative distributions of features obtained from pre-trained vision language models. In addition, we introduce a learning-based strategy that enables training from unlabeled and potentially contradictory data by aligning inconsistent observations into a coherent relative distribution. For the downstream object search task, we propose an agent that leverages predicted feature distributions as a semantic prior to guide exploration toward regions with a high likelihood of containing the object. We present extensive evaluations demonstrating that ProReFF captures meaningful relative feature distributions in natural scenes and provides insight into the impact of our proposed alignment step. We further evaluate the performance of our search agent in 100 challenges in the Matterport3D simulator, comparing with feature-based baselines and human participants. The proposed agent is 20% more efficient than the strongest baseline and achieves up to 80% of human performance.

Gabriele Somaschini, Adrian Röfer, Abhinav Valada

The authors are with the Department of Computer Science, University of Freiburg.

Robotic Object Search Robotics Unsupervised Learning

Mar 8, 2026

Preference-Conditioned Reinforcement Learning for Space-Time Efficient Online 3D Bin Packing

Robotic bin packing is widely deployed in warehouse automation, with current systems achieving robust performance through heuristic and learning-based strategies. These systems must balance compact placement with rapid execution, where selecting alternative items or reorienting them can improve space utilization but introduce additional time. We propose a selection-based formulation that explicitly reasons over this trade-off: at each step, the robot evaluates multiple candidate actions, weighing expected packing benefit against estimated operational time. This enables time-aware strategies that selectively accept increased operational time when it yields meaningful spatial improvements. Our method, STEP (Space-Time Efficient Packing), uses a preference-conditioned, Transformer-based reinforcement learning policy, and allows generalization across candidate set sizes and integration with standard placement modules. It achieves a 44% reduction in operational time without compromising packing density. Additional material is available at https://step-packing.github.io.

Nikita Sarawgi, Omey M. Manyar, Fan Wang +3

Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA. · Amazon Robotics, North Reading, MA, USA.

Robotics Multi-Objective Reinforcement Learning

Mar 8, 2026

Adaptive Capacity Allocation for Vision Language Action Fine-tuning

Vision language action models (VLAs) are increasingly used for Physical AI, but deploying a pre-trained VLA model to unseen environments, embodiments, or tasks still requires adaptation. Parameter-efficient fine-tuning (PEFT), especially LoRA, is common for VLA policies, yet the exposed capacity knob, the rank, does not transfer uniformly: robotics transfer exhibits a higher and task-varying intrinsic rank than language fine-tuning. Small ranks suffice for LLMs (e.g.,

r \in \{4, 8\}

), while spectral analyses indicate VLAs may require much larger ranks (e.g.,

r \approx 128

) or near-full rank, a mismatch that worsens in multi-task settings. We present LoRA-SP (Select-Prune), a rank-adaptive fine-tuning method that replaces fixed-rank updates with input- and layer-wise capacity. LoRA-SP uses an SVD-style parameterization with a small router whose nonnegative scores act as singular values over a shared vector bank. The active set is chosen by an energy target on the cumulative squared scores

E(k) \ge η

, providing a direct link to approximation error via our spectral analysis. During training,

η

concentrates energy on a few directions and teaches the router to rely on fewer vectors while preserving accuracy. This yields compact adapters that reduce cross-task interference and improve generalization. On four real-robot manipulation tasks collected on an unseen AgileX PiPER arm, across two VLA backbones (

π_0

and SmolVLA), LoRA-SP matches or exceeds full fine-tuning with far fewer trainable parameters, and improves multi-task success by up to 31.6% over standard LoRA while remaining robust to rank choice.

Donghoon Kim, Minji Bae, Unghui Nam +4

Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea · Department of Computer Science and Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea

VLA Model Training Nonlinear LoRA Parameter-Efficient Fine-Tuning VLA Model Adaptation Low-Rank Adaptation Vision-Language-Action Models+1

Mar 7, 2026

Model-based thermal drift compensation for high-precision hexapod robot actuators

Thermal expansion is a significant source of positioning error in high-precision hexapod robots (Gough-Stewart platforms). Any variation in the temperature of the hexapod's parts induces expansion, which alters their kinematic model and reduces the robot's accuracy and repeatability. These variations may arise from internal heat sources (such as motors, encoders, and electronics) or from environmental changes. In this study, a method is proposed to anticipate and therefore correct the thermal drift of one of the hexapod precision electro-mechanical actuators. This method is based on determining a model that links the expansion state of the actuator at any given moment to the temperature of some well-chosen points on its surface. This model was initially developed theoretically. Its coefficients were then adjusted experimentally on a specific test-bench, based on a rigorous measurement campaign of actuator expansion using a high-precision interferometric measurement system. Experimental validation demonstrates a reduction of thermally induced expansion by more than 80%. This paves the way for thermal drift correction across the entire robot or similar robotics parts.

Clément Robert, Alain Vissiere, Olivier Company +3

LIRMM (Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier), UMR5506 Montpellier Univ. and CNRS, Montpellier, France. · SYMETRIE, Nîmes, France. · LCM (Laboratoire Commun de Métrologie), LNE/CNAM, Paris, France

Mar 7, 2026

VSL-Skin: Individually Addressable Phase-Change Voxel Skin for Variable-Stiffness and Virtual Joints Bridging Soft and Rigid Robots

Soft robots are compliant but often cannot support loads or hold their shape, while rigid robots provide structural strength but are less adaptable. Existing variable-stiffness systems usually operate at the scale of whole segments or patches, which limits precise control over stiffness distribution and virtual joint placement. This paper presents the Variable Stiffness Lattice Skin (VSL-Skin), the first system to enable individually addressable voxel-level morphological control with centimeter-scale precision. The system provides three main capabilities: nearly two orders of magnitude stiffness modulation across axial (15-1200 N/mm), shear (45-850 N/mm), bending (810^2 - 310^4 N/deg), and torsional modes with centimeter-scale spatial control; the first demonstrated 30% axial compression in phase-change systems while maintaining structural integrity; and autonomous component-level self-repair through thermal cycling, which eliminates fatigue accumulation and enables programmable sacrificial joints for predictable failure management. Selective voxel activation creates six canonical virtual joint types with programmable compliance while preserving structural integrity in non-activated regions. The platform incorporates closed-form design models and finite element analysis for predictive synthesis of stiffness patterns and joint placement. Experimental validation demonstrates 30% axial contraction, thermal switching in 75-second cycles, and cut-to-fit integration that preserves addressability after trimming. The row-column architecture enables platform-agnostic deployment across diverse robotic systems without specialized infrastructure. This framework establishes morphological intelligence as an engineerable system property and advances autonomous reconfigurable robotics.

Zihan Oliver Zeng, Jiajun An, Preston Luk +1

School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA. · Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, USA. · School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN, USA.

Robotics Soft Robotics

Mar 6, 2026

RODEO: RObotic DEcentralized Organization

Robots are improving their autonomy with minimal human supervision. However, auditable actions, transparent decision processes, and new human-robot interaction models are still missing requirements to achieve extended robot autonomy. To tackle these challenges, we propose RODEO (RObotic DEcentralized Organization), a blockchain-based framework that integrates trust and accountability mechanisms for robots. This paper formalizes Decentralized Autonomous Organizations (DAOs) for service robots. First, it provides a ROS-ETH bridge between the DAO and the robots. Second, it offers templates that enable organizations (e.g., companies, universities) to integrate service robots into their operations. Third, it provides proof-verification mechanisms that allow robot actions to be auditable. In our experimental setup, a mobile robot was deployed as a trash collector in a lab scenario. The robot collects trash and uses a smart bin to sort and dispose of it correctly. Then, the robot submits a proof of the successful operation and is compensated in DAO tokens. Finally, the robot re-invests the acquired funds to purchase battery charging services. Data collected in a three day experiment show that the robot doubled its income and reinvested funds to extend its operating time. The proof validation times of approximately one minute ensured verifiable task execution, while the accumulated robot income successfully funded up to 88 hours of future autonomous operation. The results of this research give insights about how robots and organizations can coordinate tasks and payments with auditable execution proofs and on-chain settlement.

Milan Groshev, Eduardo Castelló Ferrer

CyPhy Life, Robotics & AI Lab, School of Science & Technology, IE University, Spain

Service Robotics Robotics

Mar 6, 2026

MagRobot:An Open Simulator for Magnetically Navigated Robots

Magnetic navigation systems, including magnetic tracking systems and magnetic actuation systems, have shown great potential for occlusion-free localization and remote control of intracorporeal medical devices and robots in minimally invasive medicine, such as capsule endoscopy and cardiovascular intervention. However, the design of magnetically navigated robots remains heavily reliant on experimental prototyping, which is time-consuming and costly. Furthermore, there is a lack of a consistent experimental environment to compare and benchmark the hardware and algorithms across different magnetic navigation systems. To address these challenges, we propose the first universal open-source simulation platform to facilitate research, design and benchmarking of magnetically navigated robots. Our simulator features an intuitive graphical user interface that enables the user to efficiently design, visualize, and analyze magnetic navigation systems for both rigid and soft robots. The proposed simulator is versatile, which can simulate both magnetic actuation and magnetic tracking tasks in diverse medical applications that involve deformable anatomies. The proposed simulator provides an open development environment, where the user can load third-party anatomical models and customize both hardware and algorithms of magnetic navigation systems. The fidelity of the simulator is validated using both phantom and ex vivo experiments of magnetic navigation of a continuum robot and a capsule robot with diverse magnetic actuation setups. Three use cases of the simulator, i.e., bronchoscopy, endovascular intervention, and gastrointestinal endoscopy, are implemented to demonstrate the functionality of the simulator. It is shown that the configuration and algorithms of magnetic navigation systems can be flexibly designed and optimized for better performance using the simulator.

Heng Wang, Haoyu Song, Jiatao Zheng +2

South China University of Technology · Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China 511442

Robotics Simulation Robotics Robot Navigation

Mar 6, 2026

Proprioceptive Shape Estimation of Tensegrity Manipulators Using Energy Minimisation

Shape estimation is fundamental for controlling continuously bending tensegrity manipulators, yet achieving it remains a challenge. Although using exteroceptive sensors makes the implementation straightforward, it is costly and limited to specific environments. Proprioceptive approaches, by contrast, do not suffer from these limitations. So far, several methods have been proposed; however, to our knowledge, there are no proven examples of large-scale tensegrity structures used as manipulators. This paper demonstrates that shape estimation of the entire tensegrity manipulator can be achieved using only the inclination angle information relative to gravity for each strut. Inclination angle information is intrinsic sensory data that can be obtained simply by attaching an inertial measurement unit (IMU) to each strut. Experiments conducted on a five-layer tensegrity manipulator with 20 struts and a total length of 1160 mm demonstrate that the proposed method can estimate the shape with an accuracy of 2.1 % of the total manipulator length, from arbitrary initial conditions under both static conditions and maintains stable shape estimation under external disturbances.

Tufail Ahmad Bhat, Shuhei Ikemoto

Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka, Japan

Tensegrity Robotics Robotics Deformable Shape Reconstruction

Mar 6, 2026

Terrain characterization and locomotion adaptation in a small-scale lizard-inspired robot

Unlike their large-scale counterparts, small-scale robots are largely confined to laboratory environments and are rarely deployed in real-world settings. As robot size decreases, robot-terrain interactions fundamentally change; however, there remains a lack of systematic understanding of what sensory information small-scale robots should acquire and how they should respond when traversing complex natural terrains. To address these challenges, we develop a Small-scale, Intelligent, Lizard-inspired, Adaptive Robot (SILA Bot) capable of adapting to diverse substrates. We use granular media of varying depths as a controlled yet representative terrain paradigm. We show that the optimal body movement pattern (ranging from standing-wave bending that assists limb retraction on flat ground to traveling-wave undulation that generates thrust in deep granular media) can be parameterized and approximated as a linear function of granular depth. Furthermore, proprioceptive signals, such as joint torque, provide sufficient information to estimate granular depth via a K-Nearest Neighbors classifier, achieving 95% accuracy. Leveraging these relationships, we design a simple linear feedback controller that modulates body phase and substantially improves locomotion performance on terrains with unknown depth. Together, these results establish a principled framework for perception and control in small-scale locomotion and enable effective terrain-adaptive locomotion while maintaining low computational complexity.

Duncan Andrews, Landon Zimmerman, Evan Martin +2

Locomotion in Biology and Robotics (LiBR) Lab at Penn State University, University Park, PA 16802

Bioinspired Robotics Robotics Terrain-Aware Robot Locomotion Robot Locomotion Robotic Control Granular Terrain Locomotion

Mar 5, 2026

Vision-Language System using Open-Source LLMs for Gestures in Medical Interpreter Robots

Effective communication is vital in healthcare, especially across language barriers, where non-verbal cues and gestures are critical. This paper presents a privacy-preserving vision-language framework for medical interpreter robots that detects specific speech acts (consent and instruction) and generates corresponding robotic gestures. Built on locally deployed open-source models, the system utilizes a Large Language Model (LLM) with few-shot prompting for intent detection. We also introduce a novel dataset of clinical conversations annotated for speech acts and paired with gesture clips. Our identification module achieved 0.90 accuracy, 0.93 weighted precision, and a 0.91 weighted F1-Score. Our approach significantly improves computational efficiency and, in user studies, outperforms the speech-gesture generation baseline in human-likeness while maintaining comparable appropriateness.

Thanh-Tung Ngo, Emma Murphy, Robert J. Ross

Technological University Dublin Dublin, Ireland

Healthcare Robotics Human-Robot Interaction Robotics Co-Speech Gesture Generation VLMs for Robotics