Robotics

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Overview

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.

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

Feb 22, 2026

Design and Control of Modular Magnetic Millirobots for Multimodal Locomotion and Shape Reconfiguration

Modular small-scale robots offer the potential for on-demand assembly and disassembly, enabling task-specific adaptation in dynamic and constrained environments. However, existing modular magnetic platforms often depend on workspace collisions for reconfiguration, employ bulky three-dimensional electromagnetic systems, and lack robust single-module control, which limits their applicability in biomedical settings. In this work, we present a modular magnetic millirobotic platform comprising three cube-shaped modules with embedded permanent magnets, each designed for a distinct functional role: a free module that supports self-assembly and reconfiguration, a fixed module that enables flip-and-walk locomotion, and a gripper module for cargo manipulation. Locomotion and reconfiguration are actuated by programmable combinations of time-varying two-dimensional uniform and gradient magnetic field inputs. Experiments demonstrate closed-loop navigation using real-time vision feedback and A* path planning, establishing robust single-module control capabilities. Beyond locomotion, the system achieves self-assembly, multimodal transformations, and disassembly at low field strengths. Chain-to-gripper transformations succeeded in 90% of trials, while chain-to-square transformations were less consistent, underscoring the role of module geometry in reconfiguration reliability. These results establish a versatile modular robotic platform capable of multimodal behavior and robust control, suggesting a promising pathway toward scalable and adaptive task execution in confined environments.

Erik Garcia Oyono, Jialin Lin, Dandan Zhang

Department of Bioengineering, Imperial College London, London, United Kingdom.

Modular RoboticsRoboticsRobot Locomotion
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Feb 22, 2026

Design, Locomotion, and Control of Amphibious Robots: Recent Advances

Amphibious robots, operating seamlessly across land and water, are advancing applications in conservation, disaster response, and defense. Their performance depends on locomotion mechanisms, actuation technologies, and sensor-control integration. This review highlights recent progress in these areas, examining movement strategies, material-based actuators, and control systems for autonomy and adaptability. Challenges and opportunities are outlined to guide future research toward more efficient, resilient, and multifunctional amphibious robots.

Yi Jin, Chang Liu, Roger D. Quinn +2

Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH 44106, USA · John A. Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA, 02134, USA · Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, OH 44106, USA +2

RoboticsRobot LocomotionRobotic Control
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Feb 21, 2026

Habilis-ββ: A Fast-Motion and Long-Lasting On-Device Vision-Language-Action Model

We introduce Habilis-ββ, a fast-motion and long-lasting on-device vision-language-action (VLA) model designed for real-world deployment. Current VLA evaluation remains largely confined to single-trial success rates under curated resets, which fails to capture the fast-motion and long-lasting capabilities essential for practical operation. To address this, we introduce the Productivity-Reliability Plane (PRP), which evaluates performance through Tasks per Hour (TPH) and Mean Time Between Intervention (MTBI) under a continuous-run protocol that demands both high-speed execution and sustained robustness. Habilis-ββ achieves high performance by integrating language-free pre-training on large-scale play data for robust interaction priors with post-training on cyclic task demonstrations that capture state drift across consecutive task iterations. The system further employs ESPADA for phase-adaptive motion shaping to accelerate free-space transit, utilizes rectified-flow distillation to enable high-frequency control on edge devices, and incorporates classifier-free guidance (CFG) as a deployment-time knob to dynamically balance instruction adherence and learned interaction priors. In 1-hour continuous-run evaluations, Habilis-ββ achieves strong performance under the PRP metrics, compared to π0.5π_{0.5} in both simulation and real-world environments. In simulation, Habilis-ββ achieves 572.6 TPH and 39.2 s MTBI (vs. 120.5 TPH and 30.5 s for π0.5π_{0.5}), while in a real-world humanoid logistics workflow it achieves 124 TPH and 137.4 s MTBI (vs. 19 TPH and 46.1 s for π0.5π_{0.5}). Finally, Habilis-ββ achieves the highest reported performance on the standard RoboTwin 2.0 leaderboard across representative tasks, validating its effectiveness in complex manipulation scenarios.

Tommoro Robotics, :, Jesoon Kang +22

Tommoro Robotics

Vision-Language-Action ModelsRoboticsEfficient VLA Model InferenceRobustness of VLA ModelsRobot Manipulation Benchmarks
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Feb 19, 2026

"It's like a pet...but my pet doesn't collect data about me": Multi-person Households' Privacy Design Preferences for Household Robots

Household robots boasting mobility, more sophisticated sensors, and powerful processing models have become increasingly prevalent in the commercial market. However, these features may expose users to unwanted privacy risks, including unsolicited data collection and unauthorized data sharing. While security and privacy researchers thus far have explored people's privacy concerns around household robots, literature investigating people's preferred privacy designs and mitigation strategies is still limited. Additionally, the existing literature has not yet accounted for multi-user perspectives on privacy design and household robots. We aimed to fill this gap by conducting in-person participatory design sessions with 15 households to explore how they would design a privacy-aware household robot based on their concerns and expectations. We found that participants did not trust that robots, or their respective manufacturers, would respect the data privacy of household members or operate in a multi-user ecosystem without jeopardizing users' personal data. Based on these concerns, they generated designs that gave them authority over their data, contained accessible controls and notification systems, and could be customized and tailored to suit the needs and preferences of each user over time. We synthesize our findings into actionable design recommendations for robot manufacturers and developers.

Jennica Li, Shirley Zhang, Dakota Sullivan +4

University of Wisconsin–Madison · Koç University Istanbul

RoboticsHuman-Robot Interaction
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Feb 18, 2026

Smooth trajectory generation and hybrid B-splines-Quaternions based tool path interpolation for a 3T1R parallel kinematic milling robot

This paper presents a smooth trajectory generation method for a four-degree-of-freedom parallel kinematic milling robot. The proposed approach integrates B-spline and Quaternion interpolation techniques to manage decoupled position and orientation data points. The synchronization of orientation and arc-length-parameterized position data is achieved through the fitting of smooth piece-wise Bezier curves, which describe the non-linear relationship between path length and tool orientation, solved via sequential quadratic programming. By leveraging the convex hull properties of Bezier curves, the method ensures spatial and temporal separation constraints for multi-agent trajectory generation. Unit quaternions are employed for orientation interpolation, providing a robust and efficient representation that avoids gimbal lock and facilitates smooth, continuous rotation. Modifier polynomials are used for position interpolation. Temporal trajectories are optimized using minimum jerk, time-optimal piece-wise Bezier curves in two stages: task space followed by joint space, implemented on a low-cost microcontroller. Experimental results demonstrate that the proposed method offers enhanced accuracy, reduced velocity fluctuations, and computational efficiency compared to conventional interpolation methods.

Sina Akhbari, Mehran Mahboubkhah

Intelligent Automation Centre, Wolfson School of Mechanical, Electrical & Manufacturing Engineering, Loughborough University, Loughborough, UK · Department of Mechanical Engineering, University of Tabriz, 51666-16471, Tabriz, Iran

Trajectory GenerationRoboticsRobot Motion GenerationIndustrial RoboticsTrajectory Optimization
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Feb 18, 2026

Machine Learning Driven Prediction of the Behavior of Biohybrid Actuators

Skeletal muscle-based biohybrid actuators have proved to be a promising component in soft robotics, offering efficient movement. However, their intrinsic biological variability and nonlinearity pose significant challenges for controllability and predictability. To address these issues, this study investigates the application of supervised learning, a form of machine learning, to model and predict the behavior of biohybrid machines (BHMs), focusing on a muscle ring anchored on flexible polymer pillars. First, static prediction models (i.e., random forest and neural network regressors) are trained to estimate the maximum exerted force achieved from input variables such as muscle sample, electrical stimulation parameters, and baseline exerted force. Second, a dynamic modeling framework, based on Long Short-Term Memory networks, is developed to serve as a digital twin, replicating the time series of exerted forces observed in response to electrical stimulation. Both modeling approaches demonstrate high predictive accuracy. The best performance of the static models is characterized by R2 of 0.9425, whereas the dynamic model achieves R2 of 0.9956. The static models can enable optimization of muscle actuator performance for targeted applications and required force outcomes, while the dynamic model provides a foundation for developing robustly adaptive control strategies in future biohybrid robotic systems.

Michail-Antisthenis Tsompanas, Marco Perez Hernandez, Faisal Abdul-Fattah +9

Unconventional Computing Laboratory & School of Computing & Creative Technologies, College of Arts, Technology and Environment, University of the West of England, Bristol, BS16 1QY, United Kingdom · School of Computing & Creative Technologies, College of Arts, Technology and Environment, University of the West of England, Bristol, BS16 1QY, United Kingdom · Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute for Science and Technology (BIST), Barcelona, Spain +3

RoboticsSoft Robotics
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Feb 17, 2026

Hybrid F' and ROS2 Architecture for Vision-Based Autonomous Flight: Design and Experimental Validation

Autonomous aerospace systems require architectures that balance deterministic real-time control with advanced perception capabilities. This paper presents an integrated system combining NASA's F' flight software framework with ROS2 middleware via Protocol Buffers bridging. We evaluate the architecture through a 32.25-minute indoor quadrotor flight test using vision-based navigation. The vision system achieved 87.19 Hz position estimation with 99.90% data continuity and 11.47 ms mean latency, validating real-time performance requirements. All 15 ground commands executed successfully with 100 % success rate, demonstrating robust F'--PX4 integration. System resource utilization remained low (15.19 % CPU, 1,244 MB RAM) with zero stale telemetry messages, confirming efficient operation on embedded platforms. Results validate the feasibility of hybrid flight-software architectures combining certification-grade determinism with flexible autonomy for autonomous aerial vehicles.

Abdelrahman Metwally, Monijesu James, Aleksey Fedoseev +3

Skolkovo Institute of Science and Technology Moscow, Russia

RoboticsUAV Navigation
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Feb 16, 2026

DM0: An Embodied-Native Vision-Language-Action Model towards Physical AI

Moving beyond the traditional paradigm of adapting internet-pretrained models to physical tasks, we present DM0, an Embodied-Native Vision-Language-Action (VLA) framework designed for Physical AI. Unlike approaches that treat physical grounding as a fine-tuning afterthought, DM0 unifies embodied manipulation and navigation by learning from heterogeneous data sources from the onset. Our methodology follows a comprehensive three-stage pipeline: Pretraining, Mid-Training, and Post-Training. First, we conduct large-scale unified pretraining on the Vision-Language Model (VLM) using diverse corpora--seamlessly integrating web text, autonomous driving scenarios, and embodied interaction logs-to jointly acquire semantic knowledge and physical priors. Subsequently, we build a flow-matching action expert atop the VLM. To reconcile high-level reasoning with low-level control, DM0 employs a hybrid training strategy: for embodied data, gradients from the action expert are not backpropagated to the VLM to preserve generalized representations, while the VLM remains trainable on non-embodied data. Furthermore, we introduce an Embodied Spatial Scaffolding strategy to construct spatial Chain-of-Thought (CoT) reasoning, effectively constraining the action solution space. Experiments on the RoboChallenge benchmark demonstrate that DM0 achieves state-of-the-art performance in both Specialist and Generalist settings on Table30.

En Yu, Haoran Lv, Jianjian Sun +46

Flow Matching for Action GenerationVision-Language-Action ModelsEmbodied AIRobotics
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Feb 16, 2026

Augmenting Human Balance with Generic Supernumerary Robotic Limbs

Supernumerary robotic limbs (SLs) have the potential to transform a wide range of human activities, yet their usability remains limited by key technical challenges, particularly in ensuring safety and achieving versatile control. Here, we address the critical problem of maintaining balance in the human-SLs system, a prerequisite for safe and comfortable augmentation tasks. Unlike previous approaches that developed SLs specifically for stability support, we propose a general framework for preserving balance with SLs designed for generic use. Our hierarchical three-layer architecture consists of: (i) a prediction layer that estimates human trunk and center of mass (CoM) dynamics, (ii) a planning layer that generates optimal CoM trajectories to counteract trunk movements and computes the corresponding SL control inputs, and (iii) a control layer that executes these inputs on the SL hardware. We evaluated the framework with ten participants performing forward and lateral bending tasks. The results show a clear reduction in stance instability, demonstrating the framework's effectiveness in enhancing balance. This work paves the path towards safe and versatile human-SLs interactions. [This paper has been submitted for publication to IEEE.]

Xuanyun Qiu, Dorian Verdel, Hector Cervantes-Culebro +2

All authors are with the Bioengineering Department, Imperial College of Science, Technology and Medicine, W12 0BZ London, United-Kingdom

RoboticsRobotic Control
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Feb 16, 2026

Analysis of a Cuspidal 6R Robot

We present a theoretical and numerical analysis of the kinematics for the "Transpressor", a cuspidal 6R robot. It admits up to 16 inverse kinematics solutions which are described geometrically. For special target poses, we provide the solutions analytically and present a simple numerical solver for the general case. Moreover, an analytical estimate of the Jacobian determinant on a path between two solutions proves cuspidality for a class of robots similar to the transpressor.

Alexander Feeß, Martin Weiß

OTH Regensburg, 93053 Regensburg, Germany

Robot KinematicsRoboticsInverse Kinematics
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Feb 16, 2026

RoboSolver: A Multi-Agent Large Language Model Framework for Solving Robotic Arm Problems

This study proposes an intelligent multi-agent framework built on LLMs and VLMs and specifically tailored to robotics. The goal is to integrate the strengths of LLMs and VLMs with computational tools to automatically analyze and solve problems related to robotic manipulators. Our developed framework accepts both textual and visual inputs and can automatically perform forward and inverse kinematics, compute velocities and accelerations of key points, generate 3D simulations of the robot, and ultimately execute motion control within the simulated environment, all according to the user's query. To evaluate the framework, three benchmark tests were designed, each consisting of ten questions. In the first benchmark test, the framework was evaluated while connected to GPT-4o, DeepSeek-V3.2, and Claude-Sonnet-4.5, as well as their corresponding raw models. The objective was to extract the forward kinematics of robots directly from textual descriptions. The results showed that the framework integrated with GPT-4o achieved the highest accuracy, reaching 0.97 in computing the final solution, whereas the raw model alone attained an accuracy of only 0.30 for the same task. Similarly, for the other two models, the framework consistently outperformed the corresponding raw models in terms of accuracy. The second benchmark test was identical to the first, except that the input was provided in visual form. In this test, the GPT-4o LLM was used alongside the Gemini 2.5 Pro VLM. The results showed that the framework achieved an accuracy of 0.93 in obtaining the final answer, which is approximately 20% higher than that of the corresponding raw model. The third benchmark test encompassed a range of robotic tasks, including simulation, control, velocity and acceleration computation, as well as inverse kinematics and Jacobian calculation, for which the framework achieved an accuracy of 0.97.

Hamid Khabazi, Ali F. Meghdari, Alireza Taheri

Social & Cognitive Robotics Laboratory, Center of Excellence in Design, Robotics, and Automation (CEDRA), Sharif University of Technology, Tehran, Iran · Chancellor, Fereshtegaan International Branch, Islamic Azad University, Tehran, Iran · Artificial Intelligence in Design and Complex Systems (AIDACS) Group, Sharif University of Technology, Tehran, Iran

Robot KinematicsRoboticsMulti-Agent LLM SystemsRobotic ControlVLMs for RoboticsRobotics Simulation
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Feb 9, 2026

Learning Human-Like Badminton Skills for Humanoid Robots

Realizing versatile and human-like performance in high-demand sports like badminton remains a formidable challenge for humanoid robotics. Unlike standard locomotion or static manipulation, this task demands a seamless integration of explosive whole-body coordination and precise, timing-critical interception. While recent advances have achieved lifelike motion mimicry, bridging the gap between kinematic imitation and functional, physics-aware striking without compromising stylistic naturalness is non-trivial. To address this, we propose Imitation-to-Interaction, a progressive reinforcement learning framework designed to evolve a robot from a "mimic" to a capable "striker." Our approach establishes a robust motor prior from human data, distills it into a compact, model-based state representation, and stabilizes dynamics via adversarial priors. Crucially, to overcome the sparsity of expert demonstrations, we introduce a manifold expansion strategy that generalizes discrete strike points into a dense interaction volume. We validate our framework through the mastery of diverse skills, including lifts and drop shots, in simulation. Furthermore, we demonstrate the first zero-shot sim-to-real transfer of anthropomorphic badminton skills to a humanoid robot, successfully replicating the kinetic elegance and functional precision of human athletes in the physical world.

Yeke Chen, Shihao Dong, Xiaoyu Ji +12

Adaptive Robotic Controls Lab (ArcLab), Department of Mechanical Engineering, The University of Hong Kong, Hong Kong SAR, China · EngineAI Robotics Technology Co., Ltd., Shenzhen, China

Humanoid Robot ControlRobotic RLRoboticsRobot Imitation Learning
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Feb 5, 2026

Affordance-Aware Interactive Decision-Making and Execution for Ambiguous Instructions

Enabling robots to explore and act in unfamiliar environments under ambiguous human instructions by interactively identifying task-relevant objects (e.g., cups or beverages for "I'm thirsty") remains challenging for existing vision-language model (VLM)-based methods, due to inefficient reasoning and limited environmental interaction. These factors hinder real-time task planning and execution. To address this, we propose Affordance-Aware Interactive Decision-Making and Execution for Ambiguous Instructions (AIDE), a human-inspired dual-stream framework that integrates interactive exploration with vision-language reasoning: mirroring the complementary deliberative and intuitive processes of human cognition, Multi-Stage Inference (MSI) serves as the decision-making stream and Accelerated Decision-Making (ADM) as the execution stream, enabling zero-shot affordance analysis and interpretation of ambiguous instructions. Experiments in simulation and real-world settings show that AIDE achieves over 80% task planning success rate and more than 95% closed-loop continuous execution accuracy at 10 Hz, surpassing existing VLM-based methods in diverse open scenarios.

Hengxuan Xu, Fengbo Lan, Zhixin Zhao +5

Department of Automation, Tsinghua University, Beijing, China · Shanghai Artificial Intelligence Laboratory, Shanghai, China · Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China +1

RoboticsRobot Task PlanningVLMs for RoboticsActive Vision for Robot Manipulation
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Feb 3, 2026

Robot Programming with Augmented Reality: The Role of Spatial Ability

Programming a robot arm requires users to interpret coordinate frames, joint rotations, and trajectories that are not directly visible. Augmented reality (AR) can make these spatial relations visible, but its benefits may depend on users' spatial ability. We conducted a randomized between-subjects experiment (N=71N=71) in which participants learned to program a physical UR5e robot using either conventional teach-pendant controls with PDF instructions or a head-mounted AR interface that displayed joints, coordinate frames, and waypoints. We measured users' spatial ability with the Mental Rotation Test and assessed subjective cognitive load and system usability. Overall, AR did not significantly improve cognitive load or usability compared with conventional instruction. However, exploratory analyses revealed a compensatory effect: spatial ability predicted higher usability and lower extraneous cognitive load in the control group, but not in the AR condition, suggesting AR mitigated the disadvantage typically faced by users with lower spatial ability. These findings point to a compensatory function of AR that should be explored to guide the design of personalized AR interfaces for human-robot interaction.

Nicolas Leins, Muriel Fischer, Malte Teichmann +2

Zuse Institute Berlin, Takustraße 7, 14195 Berlin, Germany · Weizenbaum Institute, Hardenbergstr. 32, 10623 Berlin, Germany · University of Potsdam, Am Neuen Palais 10, 14469 Potsdam, Brandenburg, Germany +1

RoboticsHuman-Robot InteractionSpatial Reasoning
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Feb 2, 2026

Bridging the Sim-to-Real Gap with multipanda_ros2: A Real-Time ROS2 Framework for Multimanual Systems

We present multipanda_ros2multipanda\_ros2, a novel open-source ROS2 architecture for multi-robot control of Franka Robotics robots. Leveraging ros2 control, this framework provides native ROS2 interfaces for controlling any number of robots from a single process. Our core contributions address key challenges in real-time torque control, including interaction control and robot-environment modeling. A central focus of this work is sustaining a 1kHz control frequency, a necessity for real-time control and a minimum frequency required by safety standards. Moreover, we introduce a controllet-feature design pattern that enables controller-switching delays of ≤2\le 2 ms, facilitating reproducible benchmarking and complex multi-robot interaction scenarios. To bridge the simulation-to-reality (sim2real) gap, we integrate a high-fidelity MuJoCo simulation with quantitative metrics for both kinematic accuracy and dynamic consistency (torques, forces, and control errors). Furthermore, we demonstrate that real-world inertial parameter identification can significantly improve force and torque accuracy, providing a methodology for iterative physics refinement. Our work extends approaches from soft robotics to rigid dual-arm, contact-rich tasks, showcasing a promising method to reduce the sim2real gap and providing a robust, reproducible platform for advanced robotics research.

Jon Škerlj, Seongjin Bien, Abdeldjallil Naceri +1

Munich Institute of Robotics and Machine Intelligence (MIRMI), Technische Universität München (TUM), Germany · Technische Universität Nürnberg (UTN), Germany · Mohamed Bin Zayed University of Artificial Intelligence, UAE

Robotic ControlRobot Dynamics IdentificationMulti-Robot SystemsRobotics SimulationSim-to-Real TransferRobotics
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Feb 2, 2026

Tilt-Ropter: A Fully Actuated Hybrid Aerial-Terrestrial Vehicle with Tilt Rotors and Passive Wheels

In this work, we present Tilt-Ropter, a fully actuated hybrid aerial-terrestrial vehicle (HATV) that integrates tilt rotors with passive wheels to enable efficient multi-modal locomotion. Unlike conventional underactuated HATVs, the fully actuated design of Tilt-Ropter allows decoupled force and torque control, improving maneuverability and ground locomotion efficiency. A unified nonlinear model predictive controller (NMPC) is developed to track reference trajectories, enforce non-holonomic constraints, and accommodate contact effects across locomotion modes, while ensuring actuator feasibility through dedicated control allocation. To address complex wheel-ground dynamics, an external wrench estimator is incorporated to provide real-time interaction wrench estimates. The system is validated through simulation and real-world experiments, including seamless air-ground transitions and trajectory tracking tasks. Experimental results demonstrate low tracking errors in both modes and reveal a 92.8% reduction in power consumption during ground locomotion compared with flight, highlighting the platform's potential for long-duration missions where energy efficiency is critical.

Ruoyu Wang, Xuchen Liu, Zongzhou Wu +3

Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong · Faculty of Engineering, The University of Hong Kong, Pokfulam, Hong Kong · Peng Cheng Laboratory, Shenzhen, Guangdong, China

Robotic ControlRobot Trajectory TrackingAerial RoboticsRobotics
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Jan 29, 2026

Macro-Scale Electrostatic Origami Motor

Origami structures have been an active area of research due to their high volume-to-mass ratio, packability, and shape adaptability. Previously-developed actuated foldable structures have either embedded linear actuators into the structure, or have attached non-folding rotary motors to the exterior structure. Further, in prior work, actuators directly embedded in the structure of folding media all contributed to linear or folding motion, not to continuous rotary motion. On the macro-scale there has not yet been a folding continuous rotary actuator. This paper details the development and testing of the first macro-scale origami rotary motor that can be folded flat, and then unfurled to operate. Using corona discharge for torque production, the prototype motor achieved an expansion ratio of over 3.8:1, reached a top speed of 1720 rpm when driven at -30 kV, and exhibited a maximum motor torque over 0.25 mNm with an active component torque density of 0.11 Nm/kg.

Alex S. Miller, Leo McElroy, Jeffrey H. Lang

Dept. of Aeronautics and Astronautics Massachusetts Institute of Technology Cambridge, USA · Independent Researcher Cambridge, USA · Dept. of Electrical Eng. and Computer Sci. Massachusetts Institute of Technology Cambridge, USA

Robotics
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Dec 27, 2025

VLA-Arena: An Open-Source Framework for Benchmarking Vision-Language-Action Models

While Vision-Language-Action models (VLAs) are rapidly advancing towards generalist robot policies, it remains difficult to quantitatively understand their limits and failure modes. To address this, we introduce a comprehensive benchmark called VLA-Arena. We propose a novel structured task design framework to quantify difficulty across three orthogonal axes: (1) Task Structure, (2) Language Command, and (3) Visual Observation. This allows us to systematically design tasks with fine-grained difficulty levels, enabling a precise measurement of model capability frontiers. For Task Structure, VLA-Arena's 170 tasks are grouped into four dimensions: Safety, Distractor, Extrapolation, and Long Horizon. Each task is designed with three difficulty levels (L0-L2), with fine-tuning performed exclusively on L0 to assess general capability. Orthogonal to this, language (W0-W4) and visual (V0-V4) perturbations can be applied to any task to enable a decoupled analysis of robustness. Our extensive evaluation of state-of-the-art VLAs reveals several critical limitations, including a strong tendency toward memorization over generalization, asymmetric robustness, a lack of consideration for safety constraints, and an inability to compose learned skills for long-horizon tasks. To foster research addressing these challenges and ensure reproducibility, we provide the complete VLA-Arena framework, including an end-to-end toolchain from task definition to automated evaluation and the VLA-Arena-S/M/L datasets for fine-tuning. Our benchmark, data, models, and leaderboard are available at https://vla-arena.github.io.

Borong Zhang, Jiahao Li, Jiachen Shen +7

Institute for Artificial Intelligence, Peking University. · Zhongguancun Academy. · PKU-PsiBot Joint Lab. +2

Long-Horizon PlanningRoboticsVision-Language-Action ModelsRobustness of VLA Models
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Dec 3, 2025

House of Dextra: Cross-embodied Co-design for Dexterous Hands

Dexterous manipulation is limited by both control and design, without consensus as to what makes manipulators best for performing dexterous tasks. This raises a fundamental challenge: how should we design and control robot manipulators that are optimized for dexterity? We present a co-design framework that learns task-specific hand morphology and complementary dexterous control policies. The framework supports 1) an expansive morphology search space including joint, finger, and palm generation, 2) scalable evaluation across the wide design space via morphology-conditioned cross-embodied control, and 3) real-world fabrication with accessible components. We evaluate the approach across multiple dexterous tasks, including in-hand rotation with simulation and real deployment. Our framework enables an end-to-end pipeline that can design, train, fabricate, and deploy a new robotic hand in under 24 hours. The full framework and generated robot hands are open-sourced and available on our website.

Kehlani Fay, Darin Anthony Djapri, Anya Zorin +6

University of California, San Diego · University of California, Santa Barbara

Robotic Hand DesignDexterous ManipulationRobot Morphology-Control Co-DesignCross-Embodiment Robot LearningRobotics
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Nov 19, 2025

Modelling and Model-Checking a ROS2 Multi-Robot System using Timed Rebeca

Model-based development accelerates prototyping, enables earlier experimentation, and ensures rigorous validation of system design intents. In multi-agent systems with complex asynchronous interactions and concurrency, formal verification, particularly model-checking, offers an automated means of confirming that desired properties hold. Timed Rebeca, an actor-based modelling language supporting reactive, concurrent, and timed behaviors, together with its model-checking tool, provides a powerful framework for this purpose. By leveraging these capabilities, Timed Rebeca can intuitively capture ROS2 node graphs, recurring physical signals, motion primitives, and other time-convertible behaviors. Nevertheless, modelling and verifying multi-robot systems entail significant challenges: abstracting intricate information, bridging the gap between discrete models and continuous system dynamics, and managing large state spaces while preserving fidelity. To address these challenges, we propose discretization strategies tailored to various data types and identify thresholds of abstraction that balance accuracy and tractability. We further introduce optimization techniques to accelerate verification. Our work demonstrates how to systematically design and verify multi-robot systems through Timed Rebeca, efficiently transform continuous dynamics into discrete models for model-checking, and maintain a practical, bidirectional flow between the abstract model and the ROS2 implementation. The accompanying Rebeca and ROS2 codebases, made openly available, serve as a foundational reference for researchers and developers aiming to model and verify advanced autonomous robotic systems.

Hiep Hong Trinh, Marjan Sirjani, Federico Ciccozzi +2

Mälardalen University, Universitetsplan 1, 721 23 Västerås, Sweden · KTH Royal Institute of Technology, Brinellvägen 8, 114 28 Stockholm, Sweden

Multi-Robot SystemsRoboticsFormal Verification
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Nov 15, 2025

Multilaminate piezoelectric polymer actuators for insect-scale softrobots

Piezoelectric ceramics enable fast, precise, and powerful actuation with integrated sensing in insect-scale robots. However, they are brittle, failing at low strains limiting its applications involving high impacts, unexpected loading, or soft robotics. To address this limitation, we investigate polyvinylidene fluoride (PVDF) as a compliant alternative: it tolerates substantially larger strains, but its low piezoelectric coefficient limits the output of conventional single-layer actuators. We demonstrate a novel manufacturing process for multilayer PVDF actuators that utilizes off the shelf materials and provides parallel bussing of signal voltage to reduce integration complexity. Using first principles models validated with experiments, we show a tradeoff between force (linear or superlinear increase with layer count) and displacement (cubic decrease with layer count) for the actuator geometry chosen. Our work positions PVDF actuation an optimization problem for future robotic applications with clear design knobs available to meet performance demands. Further, we show that using thinner PVDF layers within a stack increases the specific power density of the actuators making them ideal for size, weight, and power (SWAP) limitations in small scale robotics. Among the actuators fabricated, >9 mm of free deflection, >20 mN of blocked force, and >500 Hz bandwidth were achieved. To illustrate their potential in future robust robotic platforms, we integrate our actuators into a low-profile insect-scale robot that leverages resonance to achieve planar locomotion while being robust to large external impacts.

Nicholas Gunter, Heiko Kabutz, Alexander Hedrick +1

Animal Inspired Movement and Robotics Laboratory, Paul M. Rady Department of Mechanical Engineering, University of Colorado Boulder

RoboticsSoft Robotics
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Nov 14, 2025

Humanoid Whole-Body Badminton via an Annealed Reinforcement Learning Curriculum

Humanoid robots have demonstrated strong capabilities for interacting with static scenes across locomotion and manipulation, yet dynamic real-world interactions remain challenging. As a step toward fast-moving object interactions, we present an RL training pipeline that yields a unified whole-body controller for humanoid badminton, coordinating footwork and striking without motion priors or expert demonstrations. In badminton, locomotion and striking are tightly coupled, making the final hitting objective difficult to optimize directly due to sparse rewards and conflicting gradients. We address this with an annealed curriculum that first stabilizes learning through auxiliary locomotion objectives, then progressively removes them to focus optimization on the final hitting objective. For deployment, we use an Extended Kalman Filter (EKF) to estimate and predict shuttlecock trajectories for target striking, and also develop a prediction-free variant that removes the EKF and explicit prediction. We validate the framework in simulation and on hardware. In simulation, two robots sustain a rally of 21 consecutive hits. In real-world tests with both machine-fed shuttles and human-robot rallies, the robot achieves outgoing shuttle speeds up to 19.1 m/s. Moreover, the prediction-free variant attains comparable performance to the EKF-based policy. Overall, our approach enables dynamic yet precise goal striking in humanoid badminton and suggests a path toward more dynamics-critical whole-body interaction tasks.

Chenhao Liu, Leyun Jiang, Ningyuan Tian +4

RL for RoboticsReinforcement LearningHumanoid Robot ControlCurriculum RLRobotics
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Nov 4, 2025

A Collaborative Reasoning Framework for Anomaly Diagnostics in Underwater Robotics

The safe deployment of autonomous systems in safety-critical settings requires a paradigm that combines human expertise with AI-driven analysis, especially when anomalies are unforeseen. We introduce AURA (Autonomous Resilience Agent), a collaborative framework for anomaly and fault diagnostics in robotics. AURA integrates large language models (LLMs), a high-fidelity digital twin (DT), and human-in-the-loop interaction to detect and respond to anomalous behavior in real time. The architecture uses two agents with clear roles: (i) a low-level State Anomaly Characterization Agent that monitors telemetry and converts signals into a structured natural-language problem description, and (ii) a high-level Diagnostic Reasoning Agent that conducts a knowledge-grounded dialogue with an operator to identify root causes, drawing on external sources. Human-validated diagnoses are then converted into new training examples that refine the low-level perceptual model. This feedback loop progressively distills expert knowledge into the AI, transforming it from a static tool into an adaptive partner. We describe the framework's operating principles and provide a concrete implementation, establishing a pattern for trustworthy, continually improving human-robot teams.

Markus Buchholz, Niamh Ellis, Rahaf Abu Hara +2

School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh, UK

RoboticsHuman-in-the-Loop AIHuman-Robot CollaborationUnderwater RoboticsRobotic Anomaly Detection
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Oct 2, 2025

ERUPT: An Open Toolkit for Interfacing with Robot Motion Planners in Extended Reality

We present the Extended Reality Universal Planning Toolkit (ERUPT), an extended reality (XR) system for interactive motion planning. This paper serves to introduce our open-source system to others who can use it as a base to develop immersive robot interaction applications. Our system allows users to create and dynamically reconfigure environments while they plan robot paths. ERUPT uses XR to provide a broad range of natural interaction capabilities, allowing users to grab and adjust objects in the environment similar to interaction in the real world, rather than using a mouse and keyboard with the scene projected onto a 2D computer screen. Our system integrates with MoveIt, a manipulation planning framework, allowing users to send motion planning requests and visualize the resulting robot paths in virtual or augmented reality. We provide a broad range of interaction modalities, allowing users to modify objects in the environment and interact with a virtual robot. Our system allows operators to visualize robot motions, ensuring desired behavior as it moves throughout the environment, without risk of collisions within a virtual space, and to then deploy planned paths on physical robots in the real world. Code can be found at https://github.com/parasollab/erupt.

Isaac Ngui, Courtney McBeth, André Santos +6

Parasol Lab, Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, Champaign, IL 61820 USA · Insper, São Paulo, Brazil · Department of Computer Science at Instituto Tecnológico Autónomo de México (ITAM), Mexico City, Mexico

RoboticsHuman-Robot InteractionRobot Motion Planning
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Oct 2, 2025

FailSafe: Reasoning and Recovery from Failures in Vision-Language-Action Models

Recent advances in robotic manipulation have integrated low-level robotic control into Vision-Language Models (VLMs), extending them into Vision-Language-Action (VLA) models. Although state-of-the-art VLAs achieve strong performance in downstream robotic applications, supported by large-scale crowd-sourced robot training data, they still inevitably encounter failures during execution. Enabling robots to reason and recover from unpredictable and abrupt failures remains a critical challenge. Existing robotic manipulation datasets, collected in either simulation or the real world, primarily provide only ground-truth trajectories, leaving robots unable to recover once failures occur. Moreover, the few datasets that address failure detection typically offer only textual explanations, which are difficult to utilize directly in VLA models. To address this gap, we introduce FailSafe, a novel failure generation and recovery system that automatically produces diverse failure cases paired with executable recovery actions. FailSafe can be easily adapted to a wide range of manipulation tasks in simulators with motion planning support, enabling scalable creation of failure-action data. To demonstrate its effectiveness, we fine-tune LLaVA-OneVision-7B (LLaVA-OV-7B) to build FailSafe-VLM. Experimental results show that FailSafe-VLM successfully helps robotic arms detect and recover from potential failures, improving the performance of three state-of-the-art VLA models (Pi-0-FAST, OpenVLA, OpenVLA-OFT) by up to 22.6% on average across several tasks in ManiSkill. Furthermore, FailSafe-VLM could generalize across different spatial configurations, camera viewpoints, object and robotic embodiments.

Zijun Lin, Jiafei Duan, Haoquan Fang +4

1Nanyang Technological University · Centre for Frontier AI Research, A*STAR · 3Allen Institute for AI +1

Robot Failure RecoverySynthetic Data Generation for RoboticsRoboticsVision-Language-Action ModelsRobustness of VLA Models
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Sep 17, 2025

TransforMARS: Fault-Tolerant Self-Reconfiguration for Arbitrarily Shaped Modular Aerial Robot Systems

Modular Aerial Robot Systems (MARS) consist of multiple drone modules that are physically bound together to form a single structure for flight. Exploiting structural redundancy, MARS can be reconfigured into different formations to mitigate unit or rotor failures and maintain stable flight. Prior work on MARS self-reconfiguration has solely focused on maximizing controllability margins to tolerate a single rotor or unit fault for rectangular-shaped MARS. We propose TransforMARS, a general fault-tolerant reconfiguration framework that transforms arbitrarily shaped MARS under multiple rotor and unit faults while ensuring continuous in-air stability. Specifically, we develop algorithms to first identify and construct minimum controllable assemblies containing faulty units. We then plan feasible disassembly-assembly sequences to transport MARS units or subassemblies to form target configuration. Our approach enables more flexible and practical feasible reconfiguration. We validate TransforMARS in challenging arbitrarily shaped MARS configurations, demonstrating substantial improvements over prior works in both the capacity of handling diverse configurations and the number of faults tolerated. The videos and source code of this work are available at https://github.com/RuiHuangNUS/TransforMARS

Rui Huang, Zhiyu Gao, Siyu Tang +4

Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583 · Department of Mechanical Engineering, National University of Singapore, Singapore 117583

Multi-Robot Motion PlanningAerial RoboticsRobot Failure RecoveryRoboticsFault-Tolerant Control
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Sep 5, 2025

Real-time autonomous magnetic microrobot navigation across dynamic and biologically relevant environments

Autonomous microrobots could enable minimally invasive interventions in confined biological environments, but their operation requires real-time navigation among moving obstacles and environmental disturbances. Here we present a closed-loop framework for autonomous magnetic microrobot navigation that separates long-range geometric planning from short-range reactive control. An analytic geometry-based planner continuously generates collision-free global routes from microscopy images, while rule-based or reinforcement-learning local controllers respond to moving obstacles before returning control to the replanned global route. The planner produces short paths with substantially lower computation time than representative graph-, sampling- and geometry-based planners, and the integrated framework operates within the video-rate control budget. We demonstrate microrobot autonomous navigation in static and dynamic microfluidic environments, including avoidance of moving particles and human red blood cells, navigation with and against fluid flow, and repeated long-duration target transitions. We further demonstrate magnetic microrobot actuation in ex vivo ocular and vascularized cortical tissues and photoacoustic detection of magnetically induced microrobot dynamics in post-mortem tissue. These results establish a modular framework for autonomous magnetic microrobot navigation in complex biological environments.

Yanda Yang, Max Sokolich, Fatma Ceren Kirmizitas +3

Department of Mechanical Engineering, University of Delaware, Newark 19716, USA. · Department of Animal and Food Sciences, University of Delaware, Newark 19716, USA. · School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA.

Robot NavigationRoboticsDynamic Obstacle AvoidanceRobot Motion Planning
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Aug 7, 2025

Do Robots Really Need Anthropomorphic Hands? A Comparison of Human and Robotic Hands

Human manipulation skills represent a pinnacle of voluntary motor functions, requiring the coordination of many degrees of freedom and the processing of high-dimensional sensor input to achieve remarkable dexterity. Thus, this study investigates whether the human hand, with its associated biomechanical properties, sensors, and control mechanisms, is an ideal that should be strived for in robotics. Do robots need anthropomorphic hands? First, characteristics of the human hand in terms of biomechanics and perception are extracted to compare them with currently commercially available robotic hands. From this comparison, research questions are derived that connect manipulation system complexity to skill repertoire size and dexterity. These questions are addressed through a systematic literature review, analyzing the manipulation capabilities demonstrated in 125 papers published between 2019 and 2025. Although complex five-fingered hands are often considered the ultimate goal for robotic manipulators, they are not necessary for all tasks. Findings indicate that in-hand manipulation does not benefit from anthropomorphic hand design, as simpler mechanisms are sufficient; however, mechanism complexity correlates with the breadth of manipulation tasks a hand can perform. Sensor integration and intelligent manipulation strategies remain underexplored, which may be due to a misalignment with hand design: instead of replicating the number of fingers and degrees of freedom, focusing on robustness and softness would allow more intelligent control and learning to exploit environmental contacts and integrate more sensors. Finally, the article argues for standardized evaluation criteria to enable the systematic comparison of hand designs and manipulation systems.

Alexander Fabisch, Wadhah Zai El Amri, Chandandeep Singh +1

Robotics Innovation Center, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI), Robert-Hooke-Straße 1, Bremen, 28359, Bremen, Germany. · Leibniz Universität Hannover, L3S Research Center, Appelstraße, Hanover, 30167, Lower Saxony, Germany.

Robotic Hand DesignDexterous ManipulationRoboticsRobotic ManipulationHumanoid Robotics
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Aug 5, 2025

Synergy Over Spiral: A Logistics 5.0 Game-Theoretic Model for Trust-Fatigue Co-regulation in Human-Cobot Order Picking

This paper investigates the critical role of trust and fatigue in human-cobot collaborative order picking, framing the challenge within the scope of Logistics 5.0: the implementation of human-robot symbiosis in smart logistics. We propose a dynamic, leader-follower Stackelberg game to model this interaction, where utility functions explicitly account for human fatigue and trust. Through agent-based simulations, we demonstrate that while a naive model leads to a "trust death spiral," a refined trust model creates a "trust synergy cycle," increasing productivity by nearly 100 percent. Finally, we show that a cobot operating in a Trust-Recovery Mode can overcome system brittleness after a disruption, reducing trust recovery time by over 75 percent compared to a non-adaptive model. Our findings provide a framework for designing intelligent cobot behaviors that fulfill the Industry 5.0 pillars of human-centricity, sustainability, and resilience.

Soumyadeep Dhar, Ariyan Kumar Saha

Department of Industrial and Systems Engineering, Indian Institute of Technology (IIT), Kharagpur, 721302, West Bengal, India. · Department of Computer Science and Engineering, RV College of Engineering, Bengaluru, 560059, Bengaluru, India.

Trust in HRIRoboticsHuman-Robot CollaborationHuman-Robot Interaction
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May 15, 2025

Hyper Yoshimura: How a slight tweak on a classical folding pattern unleashes meta-stability for deployable robots

Deployable structures inspired by origami have provided lightweight, compact, and reconfigurable solutions for various robotic and architectural applications. However, creating an integrated structural system that can effectively balance the competing requirements of high packing efficiency, simple deployment, and precise morphing into multiple load-bearing configurations remains a significant challenge. This study introduces a new class of hyper-Yoshimura origami, which exhibits a wide range of kinematically admissible and locally metastable states, including newly discovered symmetric "self-packing" and asymmetric "pop-out" states. This metastability is achieved by breaking a design rule of Yoshimura origami that has been in place for many decades. To this end, this study derives a new set of mathematically rigorous design rules and geometric formulations. Based on this, forward and inverse kinematic strategies are developed to stack hyper-Yoshimura modules into deployable booms that can approximate complex 3D shapes. Finally, this study showcases the potential of hyper-Yoshimura with a meter-scale pop-up cellphone charging station deployed at our university's bus transit station, along with a 3D-printed, scaled prototype of a space crane that can function as an object manipulator, solar tracking device, or high-load-bearing structure. These results establish hyper-Yoshimura as a promising platform for deployable and adaptable robotic systems in both terrestrial and space environments.

Ziyang Zhou, Yogesh Phalak, Vishrut Deshpande +3

Department of Mechanical Engineering, Virginia Tech,, Blacksburg & 24060, USA. · Department of Electrical Engineering and Computer Science, University of Wyoming, Laramie & 82071, USA.

Robotics
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