A System for General In-Hand Object Re-Orientation

Tao Chen, Jie Xu, Pulkit Agrawal
Proceedings of the 5th Conference on Robot Learning, PMLR 164:297-307, 2022.

Abstract

In-hand object reorientation has been a challenging problem in robotics due to high dimensional actuation space and the frequent change in contact state between the fingers and the objects. We present a simple model-free framework that can learn to reorient objects with both the hand facing upwards and downwards. We demonstrate the capability of reorienting over $2000$ geometrically different objects in both cases. The learned policies show strong zero-shot transfer performance on new objects. We provide evidence that these policies are amenable to real-world operation by distilling them to use observations easily available in the real world. The videos of the learned policies are available at: https://taochenshh.github.io/projects/in-hand-reorientation.

Cite this Paper

BibTeX
@InProceedings{pmlr-v164-chen22a, title = {A System for General In-Hand Object Re-Orientation}, author = {Chen, Tao and Xu, Jie and Agrawal, Pulkit}, booktitle = {Proceedings of the 5th Conference on Robot Learning}, pages = {297--307}, year = {2022}, editor = {Faust, Aleksandra and Hsu, David and Neumann, Gerhard}, volume = {164}, series = {Proceedings of Machine Learning Research}, month = {08--11 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v164/chen22a/chen22a.pdf}, url = {https://proceedings.mlr.press/v164/chen22a.html}, abstract = {In-hand object reorientation has been a challenging problem in robotics due to high dimensional actuation space and the frequent change in contact state between the fingers and the objects. We present a simple model-free framework that can learn to reorient objects with both the hand facing upwards and downwards. We demonstrate the capability of reorienting over $2000$ geometrically different objects in both cases. The learned policies show strong zero-shot transfer performance on new objects. We provide evidence that these policies are amenable to real-world operation by distilling them to use observations easily available in the real world. The videos of the learned policies are available at: https://taochenshh.github.io/projects/in-hand-reorientation.} }
Endnote
%0 Conference Paper %T A System for General In-Hand Object Re-Orientation %A Tao Chen %A Jie Xu %A Pulkit Agrawal %B Proceedings of the 5th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2022 %E Aleksandra Faust %E David Hsu %E Gerhard Neumann %F pmlr-v164-chen22a %I PMLR %P 297--307 %U https://proceedings.mlr.press/v164/chen22a.html %V 164 %X In-hand object reorientation has been a challenging problem in robotics due to high dimensional actuation space and the frequent change in contact state between the fingers and the objects. We present a simple model-free framework that can learn to reorient objects with both the hand facing upwards and downwards. We demonstrate the capability of reorienting over $2000$ geometrically different objects in both cases. The learned policies show strong zero-shot transfer performance on new objects. We provide evidence that these policies are amenable to real-world operation by distilling them to use observations easily available in the real world. The videos of the learned policies are available at: https://taochenshh.github.io/projects/in-hand-reorientation.
APA
Chen, T., Xu, J. & Agrawal, P.. (2022). A System for General In-Hand Object Re-Orientation. Proceedings of the 5th Conference on Robot Learning, in Proceedings of Machine Learning Research 164:297-307 Available from https://proceedings.mlr.press/v164/chen22a.html.

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