5 papers
Hybrid Diffusion Policies with Projective Geometric Algebra for Efficient Robot Manipulation Learning
Xiatao Sun, Yuxuan Wang, Shuo Yang +2
Diffusion policies are a powerful paradigm for robot learning, but their training is often inefficient. A key reason is that networks must relearn fundamental spatial concepts, suc…
Towards Zero-Knowledge Task Planning via a Language-based Approach
Liam Merz Hoffmeister, Brian Scassellati, Daniel Rakita
In this work, we introduce and formalize the Zero-Knowledge Task Planning (ZKTP) problem, i.e., formulating a sequence of actions to achieve some goal without task-specific knowled…
PRISM-DP: Spatial Pose-based Observations for Diffusion-Policies via Segmentation, Mesh Generation, and Pose Tracking
Xiatao Sun, Yinxing Chen, Daniel Rakita
Diffusion policies generate robot motions by learning to denoise action-space trajectories conditioned on observations. These observations are commonly streams of RGB images, whose…
Optimizing Active Perception for Learning Simultaneous Viewpoint Selection and Manipulation with Diffusion Policy
Xiatao Sun, Francis Fan, Yinxing Chen +1
Robotic manipulation tasks often rely on static cameras for perception, which can limit flexibility, particularly in scenarios like robotic surgery and cluttered environments where…
Dynamic Rank Adjustment in Diffusion Policies for Efficient and Flexible Training
Xiatao Sun, Shuo Yang, Yinxing Chen +3
Diffusion policies trained via offline behavioral cloning have recently gained traction in robotic motion generation. While effective, these policies typically require a large numb…