An Empirical Study and Analysis of Learning Generalizable Manipulation Skill in the SAPIEN Simulator
This work addresses the challenge of robot manipulation skill generalization in simulation, but it is incremental as it builds on existing methods for a specific competition.
The paper tackles the problem of learning generalizable manipulation skills in robotics by developing an end-to-end pipeline that extracts point cloud features and uses a transformer-based network to predict actions, achieving a promising ranking on the SAPIEN ManiSkill Challenge 2021 leaderboard.
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.