RL for Legged Locomotion

RL: Reinforcement Learning

This field studies how quadruped and humanoid robots can learn coordinated walking, running, jumping, and navigation while staying balanced and managing contact with the ground. Common approaches train actor–critic policies such as PPO in simulation, using reward shaping and curricula; others add motion-imitation priors, perception, learned dynamics, or model-predictive control. Researchers test policies in simulators and on hardware across settings such as stairs, gaps, rough or deformable terrain, payloads, and external pushes, often using teacher–student distillation to transfer privileged simulation training to real sensors. A central challenge is achieving reliable, safe sim-to-real transfer and broad terrain or morphology generalization without sacrificing efficiency, natural motion, or energy use.

Latest papers

166 papers33 papers in the last four weeks, up 106% on the four weeks before. 0.2% of all new papers.

Learning Hip Exoskeleton Control Policy via Predictive Neuromusculoskeletal Simulation
Mar 4, 2026Ilseung Park, Changseob Song, Inseung KangHip Exoskeleton Control Robotic Control
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213 USA.
CMoE: Contrastive Mixture of Experts for Motion Control and Terrain Adaptation of Humanoid Robots
Mar 3, 2026Shihao Ma, Hongjin Chen, Zijun Xu +6Humanoid Robot Locomotion Terrain-Aware Robot Locomotion
College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, China, 200433
Watch Your Step: Learning Semantically-Guided Locomotion in Cluttered Environment
Mar 3, 2026Denan Liang, Yuan Zhu, Ruimeng Liu +3Legged Robotics RL for Legged Locomotion
Learning Thermal-Aware Locomotion Policies for an Electrically-Actuated Quadruped Robot
Mar 2, 2026Letian Qian, Yuhang Wan, Shuhan Wang +1Robot Locomotion Quadruped Locomotion
State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China
Learning Transferability: A Two-Stage Reinforcement Learning Approach for Enhancing Quadruped Robots' Performance in U-Shaped Stair Climbing
Feb 16, 2026Baixiao Huang, Baiyu Huang, Yu HouRL for Legged Locomotion Quadruped Locomotion
Independent Researcher, 7200 Bollinger Rd, Apt 516, CA 95129. · Independent Researcher, 16506 82nd Pl NE, Kenmore, WA, 98028. · Western New England University, Department of Construction Management, 1215 Wilbraham Rd, Springfield, MA 01119 (corresponding author).
TRAIN: Terrain-Aware Interactive Navigation for Quadruped Robots via Reinforcement Learning
Feb 13, 2026Wei Zhu, Irfan Tito Kurniawan, Ye Zhao +1Quadruped Robot Navigation Terrain-Aware Robot Locomotion
Department of Robotics, Graduate School of Engineering, Tohoku University, 980-8579, Sendai, Japan. · Laboratory for Intelligent Decision and Autonomous Robots, Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30313, USA.
Flow Policy Gradients for Robot Control
Feb 2, 2026Brent Yi, Hongsuk Choi, Himanshu Gaurav Singh +9Robotic Control RL for Robotics
Amazon FAR · UC Berkeley · Stanford +2
PUMA: Perception-driven Unified Foothold Prior for Mobility Augmented Quadruped Parkour
Jan 22, 2026Liang Wang, Kanzhong Yao, Yang Liu +4Legged Robotics Terrain-Aware Robot Locomotion
Institute of Cyber-Systems and Control, Zhejiang University, 310027, China · Institute of Artificial Intelligence (TeleAI), China Telecom.
Agile and Generalized Legged Locomotion via Attention-Based Neural Map Encoding
Jan 13, 2026Chong Zhang, Victor Klemm, Fan Yang +1Terrain-Aware Robot Locomotion RL for Legged Locomotion
1Robotic Systems Lab, ETH Zurich, Switzerland · 2Secure, Reliable, and Intelligent Systems Lab, ETH Zurich, Switzerland · 3ETH AI Center, Switzerland
Hiking in the Wild: A Scalable Perceptive Parkour Framework for Humanoids
Jan 12, 2026Shaoting Zhu, Ziwen Zhuang, Mengjie Zhao +2Humanoid Robot Locomotion Robot Locomotion
IIIS, Tsinghua University · Shanghai Qi Zhi Institute · Department of Computer Science and Technology, Tsinghua University
Beyond Topology: A Morphological Symmetry Graph Representation for Locomotion Policy Learning
Nov 30, 2025Sizhe Wei, Xulin Chen, Fengze Xie +3RL for Legged Locomotion Equivariant GNNs
Georgia Institute of Technology · Syracuse University · California Institute of Technology
Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning
Oct 12, 2025Arthicha Srisuchinnawong, Poramate ManoonpongConstrained RL RL for Legged Locomotion
Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand. · The University of Southern Denmark, Odense, Denmark.
GeCCo -- a Generalist Contact-Conditioned Policy for Loco-Manipulation Skills on Legged Robots
Sep 22, 2025Vassil Atanassov, Wanming Yu, Siddhant Gangapurwala +2Robotic Control Legged Robotics
Dynamic Robot Systems Group, Oxford Robotics Institute, University of Oxford · Sony AI · Dyson Institute of Engineering and Technology
Physics-Guided Residual Reinforcement Learning for Humanoid Narrow-Path Traversal
Aug 28, 2025Tianchen Huang, Sisheng Chen, Wei Zhou +8Humanoid Robot Locomotion Robot Skill Learning
Institute of Humanoid Robots, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230026, China
Synergistic Action and Style Priors for Learning Legged Locomotion
Oct 4, 2024Oliver Hausdörfer, Alexander von Rohr, Éric Lefort +1RL for Legged Locomotion Robot Policy Learning
Technical University of Munich, Germany; TUM School of Computation, Information and Technology, Department of Computer Engineering, Learning Systems and Robotics Lab; Munich Institute of Robotics and Machine Intelligence.
PA-LOCO: Learning Perturbation-Adaptive Locomotion for Quadruped Robots
Jul 5, 2024Zhiyuan Xiao, Xinyu Zhang, Xiang Zhou +1RL for Legged Locomotion Quadruped Locomotion
School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 518107, P.R. China.

Mar 4, 2026J/K move · Enter open · S save

Learning Hip Exoskeleton Control Policy via Predictive Neuromusculoskeletal Simulation

Ilseung Park, Changseob Song, Inseung Kang

Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213 USA.

Hip Exoskeleton Control Robotic Control Sim-to-Real Transfer Physics-Based Simulation RL for Legged Locomotion

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Developing exoskeleton controllers that generalize across diverse locomotor conditions typically requires extensive motion-capture data and biomechanical labeling, limiting scalability beyond instrumented laboratory settings. Here, we present a physics-based neuromusculoskeletal learning framework that trains a hip-exoskeleton control policy entirely in simulation, without motion-capture demonstrations, and deploys it on hardware via policy distillation. A reinforcement learning teacher policy is trained using a muscle-synergy action prior over a wide range of walking speeds and slopes through a two-stage curriculum, enabling direct comparison between assisted and no-exoskeleton conditions. In simulation, exoskeleton assistance reduces mean muscle activation by up to 3.4% and mean positive joint power by up to 7.0% on level ground and ramp ascent, with benefits increasing systematically with walking speed. On hardware, the assistance profiles learned in simulation are preserved across matched speed-slope conditions (r: 0.82, RMSE: 0.03 Nm/kg), providing quantitative evidence of sim-to-real transfer without additional hardware tuning. These results demonstrate that physics-based neuromusculoskeletal simulation can serve as a practical and scalable foundation for exoskeleton controller development, substantially reducing experimental burden during the design phase.