8 citations · 8 across the 7 of their papers we have counts for
5 papers · 1 filter
Learning Expressive and Compositional Motion Representation via Spectral Skills
Feiyang Wu, Chenxiao Gao, Chen Yang +3
Robotic foundation models offer a promising path toward general-purpose humanoid robot control, often through hierarchical architectures. However, their effectiveness depends on th…
Structured-Diffuser: Diffusion with Task-Conditioned Structured Priors for Motion Planning
Amelie Minji Kim, Anqi Wu, Junling Mei +1
We propose Structured-Diffuser, a diffusion planner that embeds task and motion structure directly into the noise model. Unlike standard diffusion-based planners that rely on zero-…
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…
Infer and Adapt: Bipedal Locomotion Reward Learning from Demonstrations via Inverse Reinforcement Learning
Feiyang Wu, Zhaoyuan Gu, Hanran Wu +2
Enabling bipedal walking robots to learn how to maneuver over highly uneven, dynamically changing terrains is challenging due to the complexity of robot dynamics and interacted env…