1 citations · 1 across the 6 of their papers we have counts for
6 papers
Responsive Noise-Relaying Diffusion Policy: Responsive and Efficient Visuomotor Control
Zhuoqun Chen, Xiu Yuan, Tongzhou Mu +1
Imitation learning is an efficient method for teaching robots a variety of tasks. Diffusion Policy, which uses a conditional denoising diffusion process to generate actions, has de…
When Should We Prefer State-to-Visual DAgger Over Visual Reinforcement Learning?
Tongzhou Mu, Zhaoyang Li, Stanisław Wiktor Strzelecki +4
Learning policies from high-dimensional visual inputs, such as pixels and point clouds, is crucial in various applications. Visual reinforcement learning is a promising approach th…
Policy Decorator: Model-Agnostic Online Refinement for Large Policy Model
Xiu Yuan, Tongzhou Mu, Stone Tao +3
Recent advancements in robot learning have used imitation learning with large models and extensive demonstrations to develop effective policies. However, these models are often lim…
Unpacking the Individual Components of Diffusion Policy
Xiu Yuan
Imitation Learning presents a promising approach for learning generalizable and complex robotic skills. The recently proposed Diffusion Policy generates robot action sequences thro…
GSL-PCD: Improving Generalist-Specialist Learning with Point Cloud Feature-based Task Partitioning
Xiu Yuan
Generalization in Deep Reinforcement Learning (DRL) across unseen environment variations often requires training over a diverse set of scenarios. Many existing DRL algorithms strug…
Taylor Videos for Action Recognition
Lei Wang, Xiuyuan Yuan, Tom Gedeon +1
Effectively extracting motions from video is a critical and long-standing problem for action recognition. This problem is very challenging because motions (i) do not have an explic…