8 citations · 8 across the 7 of their papers we have counts for
8 papers
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…
Crayotter: Traceable Multi-Agent Workflows for Long-Form Video Editing
Lecheng Yan, Yichong Zhang, Xiantao Xu +12
Long-form video editing over heterogeneous footage requires agents to coordinate source selection, multimodal analysis, timeline construction, narration and subtitle alignment, ren…
Distributional Inverse Reinforcement Learning
Feiyang Wu, Ye Zhao, Anqi Wu
We propose a distributional framework for offline Inverse Reinforcement Learning (IRL) that jointly models uncertainty over reward functions and full distributions of returns. Unli…
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.…
Inverse Reinforcement Learning with Switching Rewards and History Dependency for Characterizing Animal Behaviors
Jingyang Ke, Feiyang Wu, Jiyi Wang +2
Traditional approaches to studying decision-making in neuroscience focus on simplified behavioral tasks where animals perform repetitive, stereotyped actions to receive explicit re…
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…