Robot Locomotion

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Overview

Robot locomotion concerns the mechanisms and control strategies that let robots walk, run, hop, roll, climb, swim, or combine these modes. Researchers use physics-based models, gait planning, model predictive control, and reinforcement learning, often combining perception or tactile sensing with proprioception to adapt to terrain and contact. Work is tested in simulation and on physical robots, with benchmarks for stability, speed, energy use, obstacle traversal, and transfer to unfamiliar settings. A central challenge is making versatile learned controllers reliable in the real world, where uncertain contacts, deformable terrain, sensor errors, and hardware limits can undermine performance.

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233 papers31 papers in the last four weeks, down 16% on the four weeks before. 0.2% of all new papers.

Mar 3, 2026

Design, Modeling and Direction Control of a Wire-Driven Robotic Fish Based on a 2-DoF Crank-Slider Mechanism

Robotic fish have attracted growing attention in recent years owing to their biomimetic design and potential applications in environmental monitoring and biological surveys. Among robotic fish employing the Body-Caudal Fin (BCF) locomotion pattern, motor-driven actuation is widely adopted. Some approaches utilize multiple servo motors to achieve precise body curvature control, while others employ a brushless motor to drive the tail via wire or rod, enabling higher oscillation and swimming speeds. However, the former approaches typically result in limited swimming speed, whereas the latter suffer from poor maneuverability, with few capable of smooth turning. To address this trade-off, we develop a wire-driven robotic fish equipped with a 2-degree-of-freedom (DoF) crank-slider mechanism that decouples propulsion from steering, enabling both high swimming speed and agile maneuvering. In this paper, we first present the design of the robotic fish, including the elastic skeleton, waterproof structure, and the actuation mechanism that realizes the decoupling. We then establish the actuation modeling and body dynamics to analyze the locomotion behavior. Furthermore, we propose a combined feedforward-feedback control strategy to achieve independent regulation of propulsion and steering. Finally, we validate the feasibility of the design, modeling, and control through a series of prototype experiments, demonstrating swimming, turning, and directional control.

Yita Wang, Chen Chen, Yicheng Chen +5

Department of Mechanical Engineering, The University of Tokyo, Tokyo, 113-8656, Japan · Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan

Robot LocomotionUnderwater RoboticsRobotic Control
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Mar 3, 2026

Wukong-Omni: Design, Modeling and Control of a Multi-mode Robot for Air, Land, and Underwater Exploration with All-in-One Propulsion Unit

In flood disaster rescue scenarios, partially submerged buildings prevent aerial robots from accessing lower levels, limiting mission effectiveness. To address this challenge, this paper presents Wukong-Omni, a novel multimode robot capable of operating across land, air, and underwater using a unified propulsion system. The system is enabled by an innovative mechanical design that allows motor reuse and improves thrust generation. Efficiency and peak thrust are enhanced through simulation and tank-based optimization. Experimental results show a 100 percent improvement in propulsion efficiency and a 150 percent increase in maximum thrust compared with direct installation methods. Dynamic models for the three operating domains are developed, and a unified cross-domain control framework is proposed. Comprehensive experiments validate stable locomotion and smooth transition across domains. Outdoor experiments further demonstrate robustness and adaptability in real-world environments.

Yufan Liu, Rixi Yu, Junjie Li +7

School of Automation, Guandong University of Technology, Guangzhou, China · School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China · Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong, China

Robot LocomotionRobotic ControlSearch-and-Rescue RoboticsAerial RoboticsUnderwater Robotics
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Mar 2, 2026

Learning Thermal-Aware Locomotion Policies for an Electrically-Actuated Quadruped Robot

Electrically-actuated quadrupedal robots possess high mobility on complex terrains, but their motors tend to accumulate heat under high-torque cyclic loads, potentially triggering overheat protection and limiting long-duration tasks. This work proposes a thermal-aware control method that incorporates motor temperatures into reinforcement learning locomotion policies and introduces thermal-constraint rewards to prevent temperature exceedance. Real-world experiments on the Unitree A1 demonstrate that, under a fixed 3 kg payload, the baseline policy triggers overheat protection and stops within approximately 7 minutes, whereas the proposed method can operate continuously for over 27 minutes without thermal interruptions while maintaining comparable command-tracking performance, thereby enhancing sustainable operational capability.

Letian Qian, Yuhang Wan, Shuhan Wang +1

State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China

Robot LocomotionQuadruped LocomotionRL for Legged LocomotionConstrained RL
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Feb 25, 2026

System Design of the Ultra Mobility Vehicle: A Driving, Balancing, and Jumping Bicycle Robot

Trials cyclists and mountain bike riders can hop, jump, balance, and drive on one or both wheels. This versatility allows them to achieve speed and energy-efficiency on smooth terrain and agility over rough terrain. Inspired by these athletes, we present the design and control of a robotic platform, Ultra Mobility Vehicle (UMV), which combines a bicycle and a reaction mass to move dynamically with minimal actuated degrees of freedom. We employ a simulation-driven design optimization process to synthesize a spatial linkage topology with a focus on vertical jump height and momentum-based balancing on a single wheel contact. Using a constrained Reinforcement Learning (RL) framework, we demonstrate zero-shot transfer of diverse athletic behaviors, including track-stands, jumps, wheelies, rear wheel hopping, and front flips. This 23.5 kg robot is capable of high speeds (8 m/s) and jumping on and over large obstacles (1 m tall, or 130% of the robot's nominal height).

Benjamin Bokser, Daniel Gonzalez, Aaron Preston +39

Robot LocomotionRobot Morphology-Control Co-DesignRobotic Control
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Feb 25, 2026

Jumping Control for a Quadrupedal Wheeled-Legged Robot via NMPC and DE Optimization

Quadrupedal wheeled-legged robots combine the advantages of legged and wheeled locomotion to achieve superior mobility, but executing dynamic jumps remains a significant challenge due to the additional degrees of freedom introduced by wheeled legs. This paper develops a mini-sized wheeled-legged robot for agile motion and presents a novel motion control framework that integrates the Nonlinear Model Predictive Control (NMPC) for locomotion and the Differential Evolution (DE) based trajectory optimization for jumping in quadrupedal wheeled-legged robots. The proposed controller utilizes wheel motion and locomotion to enhance jumping performance, achieving versatile maneuvers such as vertical jumping, forward jumping, and backflips. Extensive simulations and real-world experiments validate the effectiveness of the framework, demonstrating a forward jump over a 0.12 m obstacle and a vertical jump reaching 0.5 m.

Xuanqi Zeng, Lingwei Zhang, Linzhu Yue +4

Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong

Robot LocomotionQuadruped Locomotion
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Feb 23, 2026

Athena: An Autonomous Open-Hardware Tracked Rescue Robot Platform

In disaster response and situation assessment, robots have great potential in reducing the risks to the safety and health of first responders. As the situations encountered and the required capabilities of the robots deployed in such missions differ wildly and are often not known in advance, heterogeneous fleets of robots are needed to cover a wide range of mission requirements. While UAVs can quickly survey the mission environment, their ability to carry heavy payloads such as sensors and manipulators is limited. UGVs can carry required payloads to assess and manipulate the mission environment, but need to be able to deal with difficult and unstructured terrain such as rubble and stairs. The ability of tracked platforms with articulated arms (flippers) to reconfigure their geometry makes them particularly effective for navigating challenging terrain. In this paper, we present Athena, an open-hardware rescue ground robot research platform with four individually reconfigurable flippers and a reliable low-cost remote emergency stop (E-Stop) solution. A novel mounting solution using an industrial PU belt and tooth inserts allows the replacement and testing of different track profiles. The manipulator with a maximum reach of 1.54m can be used to operate doors, valves, and other objects of interest. Full CAD & PCB files, as well as all low-level software, are released as open-source contributions.

Stefan Fabian, Aljoscha Schmidt, Jonas Süß +3

Technical University of Darmstadt, Computer Science Department, Simulation, Systems Optimization and Robotics Group, Germany.

Search-and-Rescue RoboticsRobot Locomotion
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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 20, 2026

Soft Surfaced Vision-Based Tactile Sensing for Bipedal Robot Applications

Legged locomotion benefits from embodied sensing, where perception emerges from the physical interaction between body and environment. We present a soft-surfaced, vision-based tactile foot sensor that endows a bipedal robot with a skin-like deformable layer that captures contact deformations optically, turning foot-ground interactions into rich haptic signals. From a contact image stream, our method estimates contact pose (position and orientation), visualizes shear, computes center of pressure (CoP), classifies terrain, and detects geometric features of the contact patch. We validate these capabilities on a tilting platform and in visually obscured conditions, showing that foot-borne tactile feedback improves balance control and terrain awareness beyond proprioception alone. These findings suggest that integrating tactile perception into legged robot feet improves stability, adaptability, and environmental awareness, offering a promising direction toward more compliant and intelligent locomotion systems. For the supplementary video, please visit: https://youtu.be/ceJiy9q_2Aw

Jaeeun Kim, Junhee Lim, Yu She

Department of Industrial Engineering, Purdue University, West Lafayette, IN 47906, USA · Department of Computer Science, Purdue University, West Lafayette, IN 47906, USA

Robotic Tactile SensingVision-Based Tactile SensingLegged RoboticsRobot LocomotionTerrain-Aware Robot Locomotion
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Feb 20, 2026

Cross-Embodiment Offline Reinforcement Learning for Heterogeneous Robot Datasets

Scalable robot policy pre-training has been hindered by the high cost of collecting high-quality demonstrations for each platform. In this study, we address this issue by uniting offline reinforcement learning (offline RL) with cross-embodiment learning. Offline RL leverages both expert and abundant suboptimal data, and cross-embodiment learning aggregates heterogeneous robot trajectories across diverse morphologies to acquire universal control priors. We perform a systematic analysis of this offline RL and cross-embodiment paradigm, providing a principled understanding of its strengths and limitations. To evaluate this offline RL and cross-embodiment paradigm, we construct a suite of locomotion datasets spanning 16 distinct robot platforms. Our experiments confirm that this combined approach excels at pre-training with datasets rich in suboptimal trajectories, outperforming pure behavior cloning. However, as the proportion of suboptimal data and the number of robot types increase, we observe that conflicting gradients across morphologies begin to impede learning. To mitigate this, we introduce an embodiment-based grouping strategy in which robots are clustered by morphological similarity and the model is updated with a group gradient. This simple, static grouping substantially reduces inter-robot conflicts and outperforms existing conflict-resolution methods.

Haruki Abe, Takayuki Osa, Yusuke Mukuta +1

The University of Tokyo, Tokyo, Japan · RIKEN Center for Advanced Intelligence Project, Tokyo, Japan

RL for RoboticsCross-Embodiment Robot LearningData-Efficient Robot LearningRobot Locomotion
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Feb 20, 2026

Whole-Brain Connectomic Graph Model Enables Whole-Body Locomotion Control in Fruit Fly

Animals perform coordinated whole-body movements under the control of neural systems shaped by brain-wide connectivity. The mapping of the whole-brain neural connections, or the connectomes, provides a natural graph for modeling sensorimotor information flow, yet its potential as a neural controller for embodied agents remains largely unexplored. Here, we introduce the Fly-connectomic Graph Model, which directly instantiates the whole-brain connectome of an adult Drosophila as a graph-structured neural controller for movements of a simulated biomechanical fruit fly via deep reinforcement learning. We achieve stable performance across diverse locomotion tasks, as well as better sample efficiency compared to both graph and non-graph baselines. Our results demonstrate a biologically informed way towards effective control policy design by translating whole-brain wiring principles into actionable architectural priors, while also improving the interpretability through dynamic information flow. This work also highlights the potential to bridge neuromechanics with embodied intelligence by providing a computational platform for investigating the sensorimotor transformation underlying animal behavior and a paradigm to advance the development of more nature-aligned intelligent systems.

Zehao Jin, Yaoye Zhu, Chen Zhang +1

Tsinghua University

Whole-Body ControlRobot Locomotion
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Feb 17, 2026

Grip as Needed, Glide on Demand: Ultrasonic Lubrication for Robotic Locomotion

Friction is the essential mediator of terrestrial locomotion, yet in robotic systems it is almost always treated as a passive property fixed by surface materials and conditions. Here, we introduce ultrasonic lubrication as a method to actively control friction in robotic locomotion. By exciting resonant structures at ultrasonic frequencies, contact interfaces can dynamically switch between "grip" and "slip" states, enabling locomotion. We developed two friction control modules, a cylindrical design for lumen-like environments and a flat-plate design for external surfaces, and integrated them into bio-inspired systems modeled after inchworm and wasp ovipositor locomotion. Both systems achieved bidirectional locomotion with nearly perfect locomotion efficiencies that exceeded 90%. Friction characterization experiments further demonstrated substantial friction reduction across various surfaces, including rigid, soft, granular, and biological tissue interfaces, under dry and wet conditions, and on surfaces with different levels of roughness, confirming the broad applicability of ultrasonic lubrication to locomotion tasks. These findings establish ultrasonic lubrication as a viable active friction control mechanism for robotic locomotion, with the potential to reduce design complexity and improve efficiency of robotic locomotion systems.

Mostafa A. Atalla, Daan van Bemmel, Jack Cummings +3

Department of BioMechanical Engineering, Delft University of Technology (TU Delft), 2628 CD Delft, The Netherlands. · Department of Cognitive Robotics, Delft University of Technology (TU Delft), 2628 CD Delft, The Netherlands.

Robot Locomotion
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Feb 17, 2026

OSCAR: An Ovipositor-Inspired Self-Propelling Capsule Robot for Colonoscopy

Self-propelling robotic capsules eliminate shaft looping of conventional colonoscopy, reducing patient discomfort. However, reliably moving within the slippery, viscoelastic environment of the colon remains a significant challenge. We present OSCAR, an ovipositor-inspired self-propelling capsule robot that translates the transport strategy of parasitic wasps into a propulsion mechanism for colonoscopy. OSCAR mechanically encodes the ovipositor-inspired motion pattern through a spring-loaded cam system that drives twelve circumferential sliders in a coordinated, phase-shifted sequence. By tuning the motion profile to maximize the retract phase relative to the advance phase, the capsule creates a controlled friction anisotropy at the interface that generates net forward thrust. We developed an analytical model incorporating a Kelvin-Voigt formulation to capture the viscoelastic stick--slip interactions between the sliders and the tissue, linking the asymmetry between advance and retract phase durations to mean thrust, and slider-reversal synchronization to thrust stability. Comprehensive force characterization experiments in ex-vivo porcine colon revealed a mean steady-state traction force of 0.85 N, closely matching the model. Furthermore, experiments confirmed that thrust generation is speed-independent and scales linearly with the phase asymmetry, in agreement with theoretical predictions, underscoring the capsule's predictable performance and scalability. In locomotion validation experiments, OSCAR demonstrated robust performance, achieving an average speed of 3.08 mm/s, a velocity sufficient to match the cecal intubation times of conventional colonoscopy. By coupling phase-encoded friction anisotropy with a predictive model, OSCAR delivers controllable thrust generation at low normal loads, enabling safer and more robust self-propelling locomotion for robotic capsule colonoscopy.

Mostafa A. Atalla, Anand S. Sekar, Remi van Starkenburg +4

M. A. Atalla, A. S. Sekar, R. van Starkenburg, D. J. Jager, A. Sakes, and P. Breedveld are with the Department of BioMechanical Engineering, Faculty of Mechanical Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands. · M. A. Atalla and M. Wiertlewski are with the Department of Cognitive Robotics, Faculty of Mechanical Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands.

Robotic EndoscopyRobot LocomotionEndoscopySurgical Robotics
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Feb 2, 2026

TTT-Parkour: Rapid Test-Time Training for Perceptive Robot Parkour

Achieving highly dynamic parkour with humanoid robots on complex terrains unseen during the pre-training stage remains a challenge in robotics. Although general locomotion policies demonstrate capabilities across broad terrain distributions, they often struggle with arbitrary and highly challenging environments. To overcome this limitation, we propose a real-to-sim-to-real framework for rapid test-time training (TTT): environment-specific policy fine-tuning in simulation before deployment on a newly encountered terrain. We adopt a two-stage end-to-end learning paradigm: a policy is first pre-trained on diverse procedurally generated terrains, followed by rapid fine-tuning on high-fidelity meshes reconstructed from real-world captures. Specifically, we develop an efficient, feed-forward, and high-fidelity geometry reconstruction pipeline using RGB-D inputs, ensuring both speed and quality during test-time training. We demonstrate that TTT-Parkour empowers humanoid robots to master complex obstacles, including wedges, stakes, boxes, trapezoids, and narrow beams. The entire pipeline, including capture, reconstruction, and test-time training, takes approximately 10 minutes for most tested terrains. Experiments show improved traversal on the tested terrains after fine-tuning, with direct sim-to-real deployment and no further policy updates on the robot.

Shaoting Zhu, Baijun Ye, Jiaxuan Wang +5

Tsinghua University · Shanghai Qi Zhi Institute · Princeton University

Robot LocomotionHumanoid Robot LocomotionRobot Policy AdaptationTerrain-Aware Robot LocomotionTest-Time Training
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Feb 1, 2026

TriphiBot: A Triphibious Robot Combining FOC-based Propulsion with Eccentric Design

Triphibious robots capable of multi-domain motion and cross-domain transitions are promising to handle complex tasks across diverse environments. However, existing designs primarily focus on dual-mode platforms, and some designs suffer from high mechanical complexity or low propulsion efficiency, which limits their application. In this paper, we propose a novel triphibious robot capable of aerial, terrestrial, and aquatic motion, by a minimalist design combining a quadcopter structure with two passive wheels, without extra actuators. To address inefficiency of ground-support motion (moving on land/seabed) for quadcopter based designs, we introduce an eccentric Center of Gravity (CoG) design that inherently aligns thrust with motion, enhancing efficiency without specialized mechanical transformation designs. Furthermore, to address the drastic differences in motion control caused by different fluids (air and water), we develop a unified propulsion system based on Field-Oriented Control (FOC). This method resolves torque matching issues and enables precise, rapid bidirectional thrust across different mediums. Grounded in the perspective of living condition and ground support, we analyse the robot's dynamics and propose a Hybrid Nonlinear Model Predictive Control (HNMPC)-PID control system to ensure stable multi-domain motion and seamless transitions. Experimental results validate the robot's multi-domain motion and cross-mode transition capability, along with the efficiency and adaptability of the proposed propulsion system.

Xiangyu Li, Tiancheng Lai, Mingwei Lai +6

State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China · Huzhou Institute of Zhejiang University, Huzhou 313000, China · Hainan Red Tower Cigarette Co., Ltd., Hainan 571100, China

Aerial RoboticsNonlinear MPCRobot LocomotionFeedback Control
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Jan 12, 2026

Hiking in the Wild: A Scalable Perceptive Parkour Framework for Humanoids

Achieving robust humanoid locomotion in complex, unstructured environments requires transitioning from reactive proprioception to proactive perception. However, integrating exteroception remains a significant challenge: existing approaches often fail to achieve high-speed, stable locomotion across diverse, unstructured terrains. In this work, we present Hiking in the Wild, an extensible, end-to-end perceptive legged parkour framework designed for robust humanoid hiking. Our approach maps depth inputs and proprioception directly to joint actions, without relying on external localization and mapping. To ensure safety and training stability, we introduce two key mechanisms: a foothold safety mechanism combining extensible Terrain Edge Detection with Foot Volume Points to reduce catastrophic slippage on edges, and a Flat Patch Sampling strategy that mitigates reward hacking by generating feasible navigation targets. Extensive experiments on the Unitree G1 humanoid robot demonstrate that our policy enables robust traversal of complex terrains at speeds up to 2.5 m/s.

Shaoting Zhu, Ziwen Zhuang, Mengjie Zhao +2

IIIS, Tsinghua University · Shanghai Qi Zhi Institute · Department of Computer Science and Technology, Tsinghua University

Humanoid Robot LocomotionRobot LocomotionTerrain-Aware Robot LocomotionRL for Legged Locomotion
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Jan 8, 2026

SKATER: Synthesized Kinematics for Advanced Traversing Efficiency on a Humanoid Robot via Roller Skate Swizzles

Although recent years have seen significant progress of humanoid robots in walking and running, the frequent foot strikes with ground during these locomotion gaits inevitably generate high instantaneous impact forces, which leads to exacerbated joint wear and poor energy utilization. Roller skating, as a sport with substantial biomechanical value, can achieve fast and continuous sliding through rational utilization of body inertia, featuring minimal kinetic energy loss. Therefore, this study proposes a novel humanoid robot with each foot equipped with a row of four passive wheels for roller skating. A deep reinforcement learning control framework is also developed for the swizzle gait with the reward function design based on the intrinsic characteristics of roller skating. The learned policy is first analyzed in simulation and then deployed on the physical robot to demonstrate the smoothness and efficiency of the swizzle gait over traditional bipedal walking gait in terms of Impact Intensity and Cost of Transport during locomotion. A reduction of 75.86%75.86\% and 63.34%63.34\% of these two metrics indicate roller skating as a superior locomotion mode for enhanced energy efficiency and joint longevity.

Junchi Gu, Feiyang Yuan, Weize Shi +6

Institute of Humanoid Robots, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230026, China.

Robot LocomotionHumanoid Robot LocomotionEnergy-Efficient Robot Locomotion
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Oct 29, 2025

Debate2Create: Robot Co-design via Multi-Agent LLM Debate

We introduce Debate2Create (D2C), a multi-agent LLM framework that formulates robot co-design as structured, iterative debate grounded in physics-based evaluation. A design agent and control agent engage in a thesis-antithesis-synthesis loop, while criterion-specific LLM judges provide multi-objective feedback to steer exploration. Across five MuJoCo locomotion benchmarks, D2C achieves the highest default-normalized score among the evaluated LLM-based and black-box baselines, with gains up to 3.2x on Ant and nearly 9x on Swimmer. Iterative debate yields 18-35% gains over compute-matched zero-shot generation, and D2C-generated rewards transfer to default morphologies in 4/5 tasks. These results suggest that structured, simulator-grounded multi-agent interaction is a useful mechanism for joint morphology-reward optimization under a fixed-topology, per-candidate-RL protocol. Project page: debate2create.github.io.

Kevin Qiu, Marek Cygan

University of Warsaw · 2IDEAS NCBR · 3Nomagic

Robot Morphology-Control Co-DesignMulti-Agent DebateRobot LocomotionMulti-Agent LLMsAgentic AI for RoboticsMulti-Agent Deliberation
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Oct 14, 2025

Pretraining in Actor-Critic Reinforcement Learning for Locomotion

The pretraining-finetuning paradigm has facilitated numerous transformative advancements in artificial intelligence research in recent years. However, in the domain of reinforcement learning (RL) for robot locomotion, individual skills are often learned from scratch despite the high likelihood that some generalizable knowledge is shared across all task-specific policies belonging to the same robot embodiment. This work aims to define a paradigm for pretraining neural network models that encapsulate such knowledge and can subsequently serve as a basis for warm-starting the RL process in classic actor-critic algorithms, such as Proximal Policy Optimization (PPO). We begin with a task-agnostic exploration-based data collection algorithm to gather diverse, dynamic transition data, which is then used to train a Proprioceptive Inverse Dynamics Model (PIDM) through supervised learning. The pretrained weights are then loaded into both the actor and critic networks to warm-start the policy optimization of actual tasks. We systematically validated our proposed method with 9 distinct robot locomotion RL environments comprising 3 different robot embodiments, showing significant benefits of this initialization strategy. Our proposed approach on average improves sample efficiency by 36.2% and task performance by 4.3% compared to random initialization. We further present key ablation studies and empirical analyses that shed light on the mechanisms behind the effectiveness of this method.

Jiale Fan, Andrei Cramariuc, Tifanny Portela +1

Section of Microengineering, EPFL, 1015 Lausanne, Switzerland · Robotic Systems Lab, ETH Zurich, 8092 Zürich, Switzerland · ETH AI Center, ETH Zurich, 8092 Zürich, Switzerland

RL for RoboticsReinforcement LearningRobot LocomotionActor-Critic Methods
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Aug 28, 2025

Uncertainty-Aware Ankle Exoskeleton Control

Lower limb exoskeletons show promise to assist human movement, but their utility is limited by controllers designed for discrete, predefined actions in controlled environments, restricting their real-world applicability. We present an uncertainty-aware control framework that enables ankle exoskeletons to operate safely across diverse scenarios by automatically disengaging when encountering unfamiliar movements. Our approach uses an uncertainty estimator to classify movements as similar (in-distribution) or different (out-of-distribution) relative to actions in the training set. We evaluated three architectures (model ensembles, autoencoders, and generative adversarial networks) on an offline dataset and tested the strongest performing architecture (ensemble of gait phase estimators) online. The online test demonstrated the ability of our uncertainty estimator to turn assistance on and off as the user transitioned between in-distribution and out-of-distribution tasks (F1: 89.2). This new framework provides a path for exoskeletons to safely and autonomously support human movement in unstructured, everyday environments.

Fatima Mumtaza Tourk, Bishoy Galoaa, Sanat Shajan +3

Department of Mechanical Engineering, Northeastern University, Boston, MA 02115 USA

Uncertainty QuantificationAnkle Exoskeleton ControlRobot LocomotionAssistive RoboticsOOD Detection
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Aug 28, 2025

Physics-Guided Residual Reinforcement Learning for Humanoid Narrow-Path Traversal

Traversing narrow paths is challenging for humanoid robots due to the sparse and safety-critical footholds required. Purely template-based or end-to-end reinforcement learning-based methods suffer from such harsh terrains. This paper proposes a two stage training framework for such narrow path traversing tasks, coupling a template-based foothold planner with a low-level foothold tracker from Stage-I training and a lightweight perception aided foothold modifier from Stage-II training. With the curriculum setup from flat ground to narrow paths across stages, the resulted controller in turn learns to robustly track and safely modify foothold targets to ensure precise foot placement over narrow paths. This framework preserves the interpretability from the physics-based template and takes advantage of the generalization capability from reinforcement learning, resulting in easy sim-to-real transfer. The learned policies outperform purely template-based or reinforcement learning-based baselines in terms of success rate, centerline adherence and safety margins. Validation on a Unitree G1 humanoid robot yields successful traversal of a 0.2m wide and 3m long beam for 20 trials without any failure.

Tianchen Huang, Sisheng Chen, Wei Zhou +8

Institute of Humanoid Robots, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230026, China

Humanoid Robot LocomotionRobot Skill LearningRobot LocomotionRL for Legged LocomotionResidual Policy Learning
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Aug 4, 2025

Tunable Leg Stiffness in a Monopedal Hopper for Energy-Efficient Vertical Hopping Across Varying Ground Profiles

We present the design and implementation of HASTA (Hopper with Adjustable Stiffness for Terrain Adaptation), a vertical hopping robot with real-time tunable leg stiffness, aimed at optimizing energy efficiency across various ground profiles (a pair of ground stiffness and damping conditions). By adjusting leg stiffness, we aim to maximize apex hopping height, a key metric for energy-efficient vertical hopping. We hypothesize that softer legs perform better on soft, damped ground by minimizing penetration and energy loss, while stiffer legs excel on hard, less damped ground by reducing limb deformation and energy dissipation. Through experimental tests and simulations, we find the best leg stiffness within our selection for each combination of ground stiffness and damping, enabling the robot to achieve maximum steady-state hopping height with a constant energy input. These results support our hypothesis that tunable stiffness improves energy-efficient locomotion in controlled experimental conditions. In addition, the simulation provides insights that could aid in the future development of controllers for selecting leg stiffness.

Rongqian Chen, Jun Kwon, Kefan Wu +1

George Washington University · General Robotics, Automation, Sensing & Perception Lab at the University of Pennsylvania · University of Connecticut

Legged RoboticsEnergy-Efficient Robot LocomotionRobot Locomotion
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May 29, 2009

Sensitive dependence of the motion of a legged robot on granular media

Legged locomotion on flowing ground ({\em e.g.} granular media) is unlike locomotion on hard ground because feet experience both solid- and fluid-like forces during surface penetration. Recent bio-inspired legged robots display speed relative to body size on hard ground comparable to high performing organisms like cockroaches but suffer significant performance loss on flowing materials like sand. In laboratory experiments we study the performance (speed) of a small (2.3 kg) six-legged robot, SandBot, as it runs on a bed of granular media (1 mm poppy seeds). For an alternating tripod gait on the granular bed, standard gait control parameters achieve speeds at best two orders of magnitude smaller than the 2 body lengths/s (≈60\approx 60 cm/s) for motion on hard ground. However, empirical adjustment of these control parameters away from the hard ground settings, restores good performance, yielding top speeds of 30 cm/s. Robot speed depends sensitively on the packing fraction φφ and the limb frequency ωω, and a dramatic transition from rotary walking to slow swimming occurs when φφ becomes small enough and/or ωω large enough. We propose a kinematic model of the rotary walking mode based on generic features of penetration and slip of a curved limb in granular media. The model captures the dependence of robot speed on limb frequency and the transition between walking and swimming modes but highlights the need for a deeper understanding of the physics of granular media.

Chen Li, Paul B. Umbanhowar, Haldun Komsuoglu +2

School of Physics, Georgia Institute of Technology, Atlanta, GA 30332 · Department of Mechanical Engineering, Northwestern University, Evanston, Illinois 60208 · Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104

Granular Terrain LocomotionRobot LocomotionLegged Robotics
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