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…
World2Act: Latent Action Post-Training from World Model Dynamics
An Dinh Vuong, Tuan Van Vo, Abdullah Sohail +6
World Models (WMs) offer a promising mechanism for post-training Vision-Language-Action (VLA) policies by providing dynamics priors that improve generalization under task and scene…
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,…
Fast and Robust Visuomotor Riemannian Flow Matching Policy
Haoran Ding, Noémie Jaquier, Jan Peters +1
Diffusion-based visuomotor policies excel at learning complex robotic tasks by effectively combining visual data with high-dimensional, multi-modal action distributions. However, d…