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*This work is partially supported by Beijing Municipal Natural Science Foundation Nos. L257009, L242025, and Natural Science Foundation of China under contracts Nos. 62495082, 62461160331.The authors are with the Key Laboratory of AI Safety of CAS, Institute of Computing Technology, Chinese Academy of Sciences (CAS), Beijing, 100190, China, and also with the University of Chinese Academy of Sciences, Beijing 100049, China. {zhanqi.xiao}@vipl.ict.ac.cn, {wangruiping,xlchen}@ict.ac.cn
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Robots can now learn manipulation affordances from human demos with better contact pose accuracy, thanks to a new pipeline that automatically infers 3D pose-centered labels.
