arXiv Machine Learning

LoComposition: Terrain-Adaptive Energy-Efficient Quadruped Locomotion without Gait Priors

arXiv:2606. 15896v1 Announce Type: cross Abstract: Learning-based quadrupedal locomotion typically relies on complex reward formulations that entangle task specification, operational limits, gait preference, and terrain adaptation within a single optimization objective.

arXiv AI

Jun 4

CoRe-MoE: Contrastive Reweighted Mixture of Experts for Multi-Terrain Humanoid Locomotion with Gait Adaptation

arXiv:2606. 04718v1 Announce Type: cross Abstract: Humans primarily rely on walking and running to traverse complex terrains, without resorting to unnecessarily complex motion patterns.

By Kailun Huang (Hong Kong University of Science and Technology), Zikang Xie (Hong Kong University of Science and Technology), Yanzhe Xie (Hong Kong University of Science and Technology), Panpan Liao (Guangdong University of Technology), Fanghai Zhang (Hong Kong University of Science and Technology), Yanheng Mai (Hong Kong University of Science and Technology), Wenhao Xu (South China Agricultural University), Yunheng Wang (Hong Kong University of Science and Technology), Renjing Xu (Hong Kong University of Science and Technology), Haohui Huang (Guangdong University of Technology)

reinforcement-learning robotics

arXiv AI

Jul 23

PGTT: Phase-Guided Terrain Traversal for Perceptive Legged Locomotion

arXiv:2510. 18348v2 Announce Type: replace-cross Abstract: State-of-the-art perceptive Reinforcement Learning controllers for legged robots typically either (i) impose oscillator-or IK-based gait priors that constrain the action space, bias policy optimization, and limit adaptability across robot morphologies, or (ii) operate "blind," making them unable to anticipate hind-leg terrain and brittle to observation noise.

By Alexandros Ntagkas, Chairi Kiourt, Konstantinos Chatzilygeroudis

reinforcement-learning robotics benchmarks safety

arXiv AI

Jul 16

Agile perceptive multi-skill locomotion for quadrupedal robots in the wild

arXiv:2607. 13579v1 Announce Type: cross Abstract: Enabling quadrupedal robots to traverse complex terrains-from rugged outdoor environments to urban landscapes-requires seamless integration of multiple motor skills, smooth transitions between gaits, and high-speed perceptive locomotion using only onboard sensors.

By Jun-Gill Kang, Jaehyun Park, Tae-Gyu Song, Joon-Ha Kim, Seungwoo Hong, Hae-Won Park

llms agents reinforcement-learning robotics

arXiv AI

Jun 19

CTS-MoE: Implicit Terrain Adaptation via Mixture-of-Experts for Perceptive Locomotion

arXiv:2606. 19633v1 Announce Type: cross Abstract: Perceptive legged locomotion over discontinuous terrain (e.

By Francisco Affonso, Matheus P. Angarola, Ana Luiza Mineiro, Aditya Potnis, Marcelo Becker, Girish Chowdhary

reinforcement-learning robotics efficiency

arXiv AI

Sep 23

Learning Energy-Efficient Air--Ground Actuation for Hybrid Robots on Stair-Like Terrain

arXiv:2603.26687v2 Announce Type: replace-cross Abstract: Hybrid aerial--ground robots can use thrust to cross obstacles that impede wheel-driven motion, but deciding how much thrust to apply during...

By Jiaxing Li, Ishaan Bhimwal, Wen Tian, Xinhang Xu, Junbin Yuan, Yuxin Guo, Sebastian Scherer, Muqing Cao

reinforcement-learning robotics benchmarks

arXiv AI

Jul 14

PUMA: Perception-driven Unified Foothold Prior for Mobility Augmented Quadruped Parkour

arXiv:2601. 15995v2 Announce Type: replace-cross Abstract: Parkour tasks for quadrupeds have emerged as a promising benchmark for agile locomotion.

By Liang Wang, Kanzhong Yao, Yang Liu, Weikai Qin, Jun Wu, Zhe Sun, Qiuguo Zhu

reinforcement-learning robotics benchmarks

arXiv AI

2d ago

PROMO: Preference-conditioned Multi-Objective Reinforcement Learning for Quadrupedal Robots

arXiv:2610.01260v1 Announce Type: cross Abstract: Quadrupedal locomotion requires balancing conflicting objectives such as command tracking, stability, and energy efficiency, yet conventional reinfor...

By Amr Mousa, Rifny Rachman, Neil Karavis, Michele Caprio, Richard Allmendinger

reinforcement-learning robotics

arXiv Machine Learning

Aug 10

Learning Fault-Tolerant Locomotion with Adaptive Gait Timing

arXiv:2608. 07328v1 Announce Type: cross Abstract: Hardware failures require legged robots to rapidly reorganize coordination and gait timing to maintain stability and mobility.

By Giovanbattista Gravina, Luca Rossini, Carlo Rizzardo, Arturo Laurenzi, Nikos Tsagarakis

reinforcement-learning robotics safety

arXiv AI

Jul 22

Training and Simulation of Quadrupedal Robot in Adaptive Stair Climbing and Descending for Indoor Firefighting: An End-to-End Reinforcement Learning Approach

arXiv:2602. 03087v2 Announce Type: replace-cross Abstract: Quadruped robots are used for primary searches during the early stages of indoor fires.

By Baixiao Huang, Baiyu Huang, Yu Hou

reinforcement-learning robotics

arXiv Computer Vision

Sep 17

LEAP: Learning Emergent Active Perception for Quadruped Navigation

arXiv:2609.17628v1 Announce Type: cross Abstract: Active perception allows autonomous agents to select their viewpoints rather than passively process the viewpoints given to them, enabling them to ta...

By \"U. Bora G\"okbakan (WILLOW), St\'ephane Caron (ISIR), Philippe Sou\`eres (LAAS-GEPETTO)

agents robotics

arXiv AI

Jul 15

GaitSpan: Growing Humanoid Locomotion from Walking to Running

arXiv:2607. 12114v1 Announce Type: cross Abstract: A humanoid that can walk should not relearn locomotion from scratch to jog or run.

By Kwan-Yee Lin, Zilin Wang, Janelle J. Liu, Stella X. Yu

robotics

arXiv AI

Aug 18

Trajectory-Level Automatic Curriculum Learning for Legged Locomotion on Unstructured Terrain

arXiv:2608. 16164v1 Announce Type: new Abstract: Training locomotion policies for complex unstructured terrain requires a curriculum to avoid early exploration failures.

By Rocky Liu, Tengyu Liu, Baoxiong Jia, Fangwei Zhong, Xinyi Tong, Hongzhao Xie, Siyuan Huang

robotics

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