2 citations · 2 across the 6 of their papers we have counts for
11 papers · 1 filter
Residual Rotation Correction using Tactile Equivariance
Yizhe Zhu, Zhang Ye, Boce Hu +4
Visuotactile policy learning augments vision-only policies with tactile input, facilitating contact-rich manipulation. However, the high cost of tactile data collection makes sampl…
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…
EquAct: An SE(3)-Equivariant Multi-Task Transformer for Open-Loop Robotic Manipulation
Xupeng Zhu, Yu Qi, Yizhe Zhu +2
Transformer architectures can effectively learn language-conditioned, multi-task 3D open-loop manipulation policies from demonstrations by jointly processing natural language instr…
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…
A Practical Guide for Incorporating Symmetry in Diffusion Policy
Dian Wang, Boce Hu, Shuran Song +2
Recently, equivariant neural networks for policy learning have shown promising improvements in sample efficiency and generalization, however, their wide adoption faces substantial…
Push-Grasp Policy Learning Using Equivariant Models and Grasp Score Optimization
Boce Hu, Heng Tian, Dian Wang +4
Goal-conditioned robotic grasping in cluttered environments remains a challenging problem due to occlusions caused by surrounding objects, which prevent direct access to the target…