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Stable Object Reorientation using Contact Plane Registration

2022-08-18 17:10:28
Richard Li, Carlos Esteves, Ameesh Makadia, Pulkit Agrawal

Abstract

We present a system for accurately predicting stable orientations for diverse rigid objects. We propose to overcome the critical issue of modelling multimodality in the space of rotations by using a conditional generative model to accurately classify contact surfaces. Our system is capable of operating from noisy and partially-observed pointcloud observations captured by real world depth cameras. Our method substantially outperforms the current state-of-the-art systems on a simulated stacking task requiring highly accurate rotations, and demonstrates strong sim2real zero-shot transfer results across a variety of unseen objects on a real world reorientation task. Project website: \url{this https URL}

Abstract (translated)

URL

https://arxiv.org/abs/2208.08962

PDF

https://arxiv.org/pdf/2208.08962


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