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ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations by Mu, Tongzhou; Ling, Zhan; Xiang, Fanbo; Yang, Derek; Li, Xuanlin; Tao, Stone; Huang, Zhiao; Jia, Zhiwei; Su, Hao is a scholarly article available to read on EtoBox.

What is ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations about?

Object manipulation from 3D visual inputs poses many challenges on building generalizable perception and policy models. However, 3D assets in existing benchmarks mostly lack the diversity of 3D shapes that align with real-world intra-class complexity in topology and geometry. Here we propose SAPIEN Manipulation Skill Benchmark (ManiSkill) to benchmark manipulation skills over diverse objects in a full-physics simulator. 3D assets in ManiSkill include large intra-class topological and geometric variations. Tasks are carefully chosen to cover distinct types of manipulation challenges. Latest progress in 3D vision also makes us believe that we should customize the benchmark so that the challenge is inviting to researchers working on 3D deep learning. To this end, we simulate a moving panoramic camera that returns ego-centric point clouds or RGB-D images. In addition, we would like ManiSkill to serve a broad set of researchers interested in manipulation research. Besides supporting the learning of policies from interactions, we also support learning-from-demonstrations (LfD) methods, by providing a large number of high-quality demonstrations (~36,000 successful trajectories, ~1.5M poin

Author

Mu, Tongzhou; Ling, Zhan; Xiang, Fanbo; Yang, Derek; Li, Xuanlin; Tao, Stone; Huang, Zhiao; Jia, Zhiwei; Su, Hao

Published

2021

Language

EN