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

• URL: http://arxiv.org/abs/2208.14646v1
• Date: Wed, 31 Aug 2022 05:45:55 GMT
• Title: An Empirical Study and Analysis of Learning Generalizable Manipulation Skill in the SAPIEN Simulator
• Authors: Kun Liu, Huiyuan Fu, Zheng Zhang, Huanpu Yin
• Abstract summary: 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 adopt these features to predict the action score of the robot simulators through a deep and wide transformer-based network.
• Score: 12.677245428522834
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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