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arXiv · 2208.14646

An Empirical Study and Analysis of Learning Generalizable Manipulation Skill in the SAPIEN Simulator

Kun Liu·Huiyuan Fu·Zheng Zhang·Huanpu Yin
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Abstract

This paper provides a brief overview of our submission to the no interaction track of SAPIEN ManiSkill Challenge 2021. Our approach follows an end-to-end pipeline which mainly consists of two steps: we first extract the point cloud features of multiple objects; then we adopt these features to predict the action score of the robot simulators through a deep and wide transformer-based network. More specially, %to give guidance for future work, to open up avenues for exploitation of learning manipulation skill, we present an empirical study that includes a bag of tricks and abortive attempts. Finally, our method achieves a promising ranking on the leaderboard. All code of our solution is available at https://github.com/liu666666/bigfish\_codes.

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Kun Liu, Huiyuan Fu, Zheng Zhang, Huanpu Yin. 2022-08-31. An Empirical Study and Analysis of Learning Generalizable Manipulation Skill in the SAPIEN Simulator. https://arxiv.org/abs/2208.14646

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