activity
20222024
most citedManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills

22 citations · 33 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2024

Planning-Guided Diffusion Policy Learning for Generalizable Contact-Rich Bimanual Manipulation

Xuanlin Li, Tong Zhao, Xinghao Zhu +3

Contact-rich bimanual manipulation involves precise coordination of two arms to change object states through strategically selected contacts and motions. Due to the inherent comple…

cs.LG2023

Reparameterized Policy Learning for Multimodal Trajectory Optimization

Zhiao Huang, Litian Liang, Zhan Ling +3

We investigate the challenge of parametrizing policies for reinforcement learning (RL) in high-dimensional continuous action spaces. Our objective is to develop a multimodal policy…

cs.RO20236 cited

On the Efficacy of 3D Point Cloud Reinforcement Learning

Zhan Ling, Yunchao Yao, Xuanlin Li +1

Recent studies on visual reinforcement learning (visual RL) have explored the use of 3D visual representations. However, none of these work has systematically compared the efficacy…

cs.RO202322 cited

ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills

Jiayuan Gu, Fanbo Xiang, Xuanlin Li +12

Generalizable manipulation skills, which can be composed to tackle long-horizon and complex daily chores, are one of the cornerstones of Embodied AI. However, existing benchmarks,…

cs.LG20225 cited

Improving Policy Optimization with Generalist-Specialist Learning

Zhiwei Jia, Xuanlin Li, Zhan Ling +3

Generalization in deep reinforcement learning over unseen environment variations usually requires policy learning over a large set of diverse training variations. We empirically ob…