6 papers
Choose What to Observe: Task-Aware Semantic-Geometric Representations for Visuomotor Policy
Haoran Ding, Liang Ma, Yaxun Yang +7
Visuomotor policies learned from demonstrations often overfit to nuisance visual factors in raw RGB observations, resulting in brittle behavior under appearance shifts such as back…
Task-Specified Compliance Bounds for Humanoids via Lipschitz-Constrained Policies
Zewen He, Yoshihiko Nakamura
Reinforcement learning (RL) has demonstrated substantial potential for humanoid bipedal locomotion and the control of complex motions. To cope with oscillations and impacts induced…
CoTaP: Compliant Task Pipeline and Reinforcement Learning of Its Controller with Compliance Modulation
Zewen He, Chenyuan Chen, Dilshod Azizov +1
Humanoid whole-body locomotion control is a critical approach for humanoid robots to leverage their inherent advantages. Learning-based control methods derived from retargeted huma…
3D-CovDiffusion: 3D-Aware Diffusion Policy for Coverage Path Planning
Chenyuan Chen, Haoran Ding, Ran Ding +6
Diffusion models have shown strong potential for robot skill learning, yet their role in coverage path planning remains underexplored. In industrial surface processing (painting, p…
Imagination at Inference: Synthesizing In-Hand Views for Robust Visuomotor Policy Inference
Haoran Ding, Anqing Duan, Zezhou Sun +2
Visual observations from different viewpoints can significantly influence the performance of visuomotor policies in robotic manipulation. Among these, egocentric (in-hand) views of…
Towards Safe Imitation Learning via Potential Field-Guided Flow Matching
Haoran Ding, Anqing Duan, Zezhou Sun +4
Deep generative models, particularly diffusion and flow matching models, have recently shown remarkable potential in learning complex policies through imitation learning. However,…