8 citations · 8 across the 6 of their papers we have counts for
7 papers
GM-Loco: Terrain-Adaptive Humanoid Locomotion on Granular Media
Junnosuke Kamohara, Feiyang Wu, Andy Ningan Zong +4
Humanoid locomotion on granular terrain remains a significant challenge due to its complex foot-terrain interaction dynamics that are difficult to model. Existing approaches either…
REFINE-DP: Diffusion Policy Fine-tuning for Humanoid Loco-manipulation via Reinforcement Learning
Zhaoyuan Gu, Yipu Chen, Zimeng Chai +11
Humanoid loco-manipulation requires coordinated task-space motion planning with stable loco-manipulation command tracking under complex robot-environment dynamics and long-horizon…
TRAIN: Terrain-Aware Interactive Navigation for Quadruped Robots via Reinforcement Learning
Wei Zhu, Irfan Tito Kurniawan, Ye Zhao +1
This study introduces TRAIN: TeRrain-Aware Interactive Navigation, a deep reinforcement learning (DRL)-based framework for quadrupedal navigation across unstructured terrain and hu…
SEEC: Stable End-Effector Control with Model-Enhanced Residual Learning for Humanoid Loco-Manipulation
Jaehwi Jang, Zhuoheng Wang, Ziyi Zhou +2
Arm end-effector stabilization is essential for humanoid loco-manipulation tasks, yet it remains challenging due to the high degrees of freedom and inherent dynamic instability of…
RL-augmented Adaptive Model Predictive Control for Bipedal Locomotion over Challenging Terrain
Junnosuke Kamohara, Feiyang Wu, Chinmayee Wamorkar +2
Model predictive control (MPC) has demonstrated effectiveness for humanoid bipedal locomotion; however, its applicability in challenging environments, such as rough and slippery te…
EmoBipedNav: Emotion-aware Social Navigation for Bipedal Robots with Deep Reinforcement Learning
Wei Zhu, Abirath Raju, Abdulaziz Shamsah +3
This study presents an emotion-aware navigation framework -- EmoBipedNav -- using deep reinforcement learning (DRL) for bipedal robots walking in socially interactive environments.…
Learn to Teach: Sample-Efficient Privileged Learning for Humanoid Locomotion over Diverse Terrains
Feiyang Wu, Xavier Nal, Jaehwi Jang +4
Humanoid robots promise transformative capabilities for industrial and service applications. While recent advances in Reinforcement Learning (RL) yield impressive results in locomo…