Compliant Legged Locomotion

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8 papers1 paper in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

Push the Limit: Learning High-Speed Sprinting and Aggressive Cornering on a Full-Scale Humanoid
Oct 10, 2026Huaxing Huang, Ruidong Li, Jinchao Han +2Adversarial Motion Prior Compliant Legged Locomotion
Noetix Robotics · Tsinghua University
Duty Factor Predicts Robust Constrained Quadrupedal Locomotion Across Gait Types
Sep 18, 2026James Zhu, David Ologan, George Ortiz +5Compliant Legged Locomotion Quadruped Locomotion
Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA · Department of Mechanical Engineering, 3 Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
Gait-Dependent Effects on Quadruped Locomotion for Load-Carrying using Passive Mechanism
Sep 10, 2026Giovanni B. Dessy, Claudio Semini, Victor BarasuolLegged Robotics Robotics
Dynamic Legged Systems, Istituto Italiano di Tecnologia, Genoa/GE, 16163, Italy
Breaking speed scaling in quadrupedal robots via Huygens' coupled-pendulum dynamics
Sep 9, 2026Yucheng Tao, Yongbin Jin, Shaowen Cheng +8Compliant Legged Locomotion Legged Robotics
Center for X-Mechanics, Zhejiang University, Hangzhou, China. · ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, China. · Mirrorme Technology Co., Ltd., Shanghai, China.
Mixture-of-Experts RL for Fault-Tolerant Legged Locomotion
Jun 24, 2026Giulio Turrisi, Ozan Pali, Luca Oneto +1Legged Robotics Mixture of Experts
Dynamic Legged Systems Laboratory, Istituto Italiano di Tecnologia (IIT), Genova, Italy. · Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi (DIBRIS), Universita di Genova, Genova, Italy
TACT-ful: Multi-Channel Terrain Affordance and Compliance Training for Payload-Robust Perceptive Humanoid Locomotion
Jun 6, 2026Thanh Ly, Truong-Duy Dang, Chien Le +5Legged Robotics Humanoid Robot Locomotion
VinRobotics, Vietnam · Center for AI Research, VinUniversity, Vietnam · Intelligent Autonomous Systems, TU Darmstadt, Germany
Energy-Efficient Quadruped Locomotion with Compliant Feet
May 14, 2026Pramod Pal, Shishir Kolathaya, Ashitava GhosalLegged Robotics Energy-Efficient Robot Locomotion
Indian Institute of Science Bangalore India · Department of Mechanical Engineering, Indian Institute of Science, Bangalore, Karnataka, India · Robert Bosch Centre for Cyber Physical Systems Bangalore India +3
SPARC: Spine with Prismatic And Revolute Compliance for Faster Quadrupedal Bounding
Oct 2, 2025Yue Wang, Yanran XuLegged Robotics Impedance Control
School of Electronic and Computer Science, University of Southampton, United Kingdom

Oct 10, 2026J/K move · Enter open · S save

Push the Limit: Learning High-Speed Sprinting and Aggressive Cornering on a Full-Scale Humanoid

Huaxing Huang, Ruidong Li, Jinchao Han, Ziyang Dong+1

Noetix Robotics · Tsinghua University

Adversarial Motion Prior Compliant Legged Locomotion Humanoid Robot Locomotion Legged Robotics RL for Legged Locomotion

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We present a learning-based framework that enables a full-scale bipedal humanoid to sprint at over 7 m/s with motion-prior-guided gait and perform aggressive cornering at high speed with a pronounced trunk lean of up to

25^\circ

. Achieving this combination is challenging: above approximately 4 m/s, periodic foot impacts induce severe, high-frequency yaw-rate oscillations intrinsic to high-speed gaits. This high-frequency temporal structure corrupts the history of proprioceptive feedback that flat-MLP policies rely on, while rendering conventional yaw-rate tracking rewards counterproductive, as they fight against the robot's natural oscillatory dynamics. Moreover, a single style prior shared across skills cannot tell which skill is commanded and inherently compromises the low-speed skill fidelity. To overcome these limitations, we adopt a causal multi-head attention (MHA) module to explicitly model the high-frequency temporal feedback. In place of the oscillatory yaw rate, we design a heading-direction reward that supervises only the low-frequency velocity direction, allowing agile turning at high speed without suppressing natural gait oscillations. We further introduce skill-conditioned multi-adversarial-motion-prior (MAMP) discriminators that condition the style reward on the commanded skill. Ablation experiments confirm that each mechanism is essential: replacing structured temporal encoding with a flat-history MLP caps the top speed; reverting to yaw-rate tracking prevents high-speed turning; and a single shared discriminator makes in-place turning collapse into a quasi-static stance. Our framework bridges the gap between raw sprinting speed and reference-motion fidelity, demonstrating stable high-speed sprinting and aggressive cornering in both simulation and real-robot deployment.