5 citations · 25 across the 28 of their papers we have counts for
31 papers
Mixture of Frames Policy: Multi-Frame Action Denoising for Bimanual Mobile Manipulation
Dian Wang, Jisang Park, Xiaomeng Xu +3
Robotic manipulation is inherently multi-frame: local actions may be simple in an end-effector frame, while transport, upright-object handling, and whole-body coordination are bett…
Pix2Act: Image-Space Manipulation Policies with Equivariant Augmentation
Haojie Huang, Linfeng Zhao, Haotian Liu +9
Representing manipulation actions as 2D trajectories in the camera plane provides a compact and interpretable basis for learning complex 3D manipulation policies. However, it also…
Action Map Policy: Learning 3D Closed-loop Manipulation via Pixel Classification
Haojie Huang, Zhang Ye, Linfeng Zhao +7
The action space poses a major challenge in robot learning, since it is often high-dimensional, can span long time horizons, and frequently admits multi-modal optimal solutions. A…
HoMMI: Learning Whole-Body Mobile Manipulation from Human Demonstrations
Xiaomeng Xu, Jisang Park, Han Zhang +6
We present Whole-Body Mobile Manipulation Interface (HoMMI), a data collection and policy learning framework that learns whole-body mobile manipulation directly from robot-free hum…
Generalizable Hierarchical Skill Learning via Object-Centric Representation
Haibo Zhao, Yu Qi, Boce Hu +9
We present Generalizable Hierarchical Skill Learning (GSL), a novel framework for hierarchical policy learning that significantly improves policy generalization and sample efficien…
3D Equivariant Visuomotor Policy Learning via Spherical Projection
Boce Hu, Dian Wang, David Klee +5
Equivariant models have recently been shown to improve the data efficiency of diffusion policy by a significant margin. However, prior work that explored this direction focused pri…