Workshop: Generalizable Policy Learning in the Physical World

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

Liu Kun · Huiyuan Fu · Zheng Zhang · huanpu yin


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: first, we 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, open up avenues for exploitation of learning manipulation skill tasks, 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**.

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