arXiv AI

Stubborn: A Streamlined and Unified Reinforcement Learning Framework for Robust Motion Tracking and Fall Recovery for Humanoids

arXiv:2606. 12814v1 Announce Type: cross Abstract: Recent reinforcement learning approaches have shown great promise in improving humanoid motion tracking performance and achieving fall recovery under disturbances.

arXiv Machine Learning

Jun 9

Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking

arXiv:2606. 08253v1 Announce Type: cross Abstract: Enabling humanoid robots to operate in complex, dynamic environments remains a critical challenge, fundamentally limited by the ability to navigate robustly, safely, and accurately.

By Alessandro Montenegro, Shihao Li, Puze Liu, Alberto Maria Metelli, Jan Peters

computer-vision reinforcement-learning robotics

arXiv Machine Learning

Jul 28

Learning Reusable Hybrid Motion Priors for Humanoid Locomotion from Motion Imitation

arXiv:2607. 24083v1 Announce Type: new Abstract: Reinforcement learning can produce robust humanoid controllers, but each new task is typically trained as a separate policy with its own reward design and training process.

By Valerio Belli (UNIROMA, UCL), Valerio Modugno (UCL), Enrico Mingo Hoffman (HUCEBOT), Fabio Amadio (HUCEBOT)

reinforcement-learning robotics efficiency

arXiv AI

Jul 2

KungfuBot: Physics-Based Humanoid Whole-Body Control for Learning Highly-Dynamic Skills

arXiv:2506. 12851v3 Announce Type: replace-cross Abstract: Humanoid robots are promising to acquire various skills by imitating human behaviors.

By Weiji Xie, Jinrui Han, Jiakun Zheng, Huanyu Li, Xinzhe Liu, Jiyuan Shi, Weinan Zhang, Chenjia Bai, Xuelong Li

robotics

arXiv AI

1d ago

Contraction-Aware Reinforcement Learning for Nonlinear Control with Statistical Robustness

arXiv:2506. 15700v2 Announce Type: replace-cross Abstract: Control contraction metrics (CCMs)-defined by Riemannian metrics under which a closed-loop system is incrementally exponentially stable-offer a constructive framework for synthesizing contracting policies in nonlinear path-tracking problems.

By Minjae Cho, Hiroyasu Tsukamoto, Huy T. Tran

reinforcement-learning robotics

arXiv AI

Jun 15

Robust Fall Recovery for Armless Bipedal-Wheeled Robots Via Force-Guided Learning

arXiv:2606. 14270v1 Announce Type: cross Abstract: Fall recovery is critical for autonomous legged locomotion.

By Haidong Hou, Zhangguo Yu, Tao Han, Hengbo Qi, Khaleel Ghazal, Yu Zhang, Yidong Du, Xuechao Chen, Fei Meng

agents reinforcement-learning robotics

arXiv Machine Learning

Jul 31

REFINE-DP: Diffusion Policy Fine-tuning for Humanoid Loco-manipulation via Reinforcement Learning

arXiv:2603. 13707v3 Announce Type: replace-cross Abstract: Humanoid loco-manipulation requires coordinated task-space motion planning with stable loco-manipulation command tracking under complex robot-environment dynamics and long-horizon tasks.

By Zhaoyuan Gu, Yipu Chen, Zimeng Chai, Alfred Cueva, Thong Nguyen, Yifan Wu, Huishu Xue, Minji Kim, Isaac Legene, Fukang Liu, KyoungMok Kim, Ayan Barula, Yongxin Chen, Ye Zhao

diffusion reinforcement-learning robotics fine-tuning

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 Machine Learning

Jun 16

ROVE: Unlocking Human Interventions for Humanoid Manipulation via Reinforcement Learning

arXiv:2606. 17011v1 Announce Type: cross Abstract: Human interventions provide crucial corrective signals for post-training Vision-Language-Action (VLA) models.

By Wei Xiao, Weiliang Tang, Yuying Ge, Hui Zhou, Yao Mu, Li Zhang, Yixiao Ge

reinforcement-learning robotics multimodal

arXiv Machine Learning

Jul 7

ADP: Adversarial Dynamics Priors for Physically Grounded Humanoid Locomotion

arXiv:2607. 03454v1 Announce Type: cross Abstract: In this paper, we propose Adversarial Dynamics Priors (ADP) for perturbation-resilient humanoid locomotion control.

By Seokju Lee, Jeongtae Lee, Jeonghyeok Lim, Jeonguk Kang, Byungwook Lee, Seungho Han, Keun Ha Choi, Dongil Park, Kyung-Soo Kim

robotics safety

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 Machine Learning

Jun 11

Critic Architecture Matters: Dual vs. Unified Critics for Humanoid Loco-Manipulation

arXiv:2606. 11891v1 Announce Type: cross Abstract: Multi-objective reinforcement learning for humanoid robots must coordinate locomotion and manipulation within a single policy.

By Mehmet Turan Yard{\i}mc{\i}

reinforcement-learning robotics fine-tuning efficiency

arXiv AI

Jul 9

LHM-Humanoid: Long-Horizon Human Motion Control for Continuous Object Transport in Cluttered Scenes

arXiv:2508. 16943v3 Announce Type: replace-cross Abstract: Physics-based human motion control can make a simulated character walk, sit, and manipulate objects with high physical realism.

By Haozhuo Zhang, Jingkai Sun, Michele Caprio, Angelo Cangelosi, Jian Tang, Shanghang Zhang, Qiang Zhang, Wei Pan

reinforcement-learning safety

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