activity
20242026
most citedThinkGrasp: A Vision-Language System for Strategic Part Grasping in Clutter

4 citations · 4 across the 2 of their papers we have counts for

collaborators

12 papers

cs.CV2026

Dissecting Embodied Abilities in Multimodal Language Models through Skill-level Evaluation and Diagnosis

Yu Qi, Haibo Zhao, Ziyu Guo +17

Understanding the capability bottlenecks of embodied multimodal large language models (MLLMs) is crucial for improving embodied agents. However, existing embodied benchmarks mainly…

cs.RO20264 cited

ThinkGrasp: A Vision-Language System for Strategic Part Grasping in Clutter

Yaoyao Qian, Xupeng Zhu, Ondrej Biza +5

Robotic grasping in cluttered environments remains a significant challenge due to occlusions and complex object arrangements. We have developed ThinkGrasp, a plug-and-play vision-l…

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

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.LG2025

Clebsch-Gordan Transformer: Fast and Global Equivariant Attention

Owen Lewis Howell, Linfeng Zhao, Xupeng Zhu +6

The global attention mechanism is one of the keys to the success of transformer architecture, but it incurs quadratic computational costs in relation to the number of tokens. On th…

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…