A new synthetic dataset with material-randomized stereo images and part-level action poses improves depth estimation and articulated object manipulation in simulation and real-world tests.
Adaafford: Learning to adapt manipulation affordance for 3d artic- ulated objects via few-shot interactions,
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GAPartManip: A Large-scale Part-centric Dataset for Material-Agnostic Articulated Object Manipulation
A new synthetic dataset with material-randomized stereo images and part-level action poses improves depth estimation and articulated object manipulation in simulation and real-world tests.