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20242026
most citedMATCH POLICY: A Simple Pipeline from Point Cloud Registration to Manipulation Policies

1 citations · 2 across the 15 of their papers we have counts for

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8 papers · 2 filters

cs.RO2025

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…

cs.RO2025

SE(3)-Equivariant Diffusion Policy in Spherical Fourier Space

Xupeng Zhu, Fan Wang, Robin Walters +1

Diffusion Policies are effective at learning closed-loop manipulation policies from human demonstrations but generalize poorly to novel arrangements of objects in 3D space, hurting…

cs.RO2025

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…

cs.RO2025

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…

cs.RO2025

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…

cs.RO2025

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…

cs.RO2025

Hierarchical Equivariant Policy via Frame Transfer

Haibo Zhao, Dian Wang, Yizhe Zhu +6

Recent advances in hierarchical policy learning highlight the advantages of decomposing systems into high-level and low-level agents, enabling efficient long-horizon reasoning and…

cs.RO2025

Coarse-to-Fine 3D Keyframe Transporter

Xupeng Zhu, David Klee, Dian Wang +5

Recent advances in Keyframe Imitation Learning (IL) have enabled learning-based agents to solve a diverse range of manipulation tasks. However, most approaches ignore the rich symm…