论文标题
SAPIEN模拟器中学习通用操纵技能的实证研究和分析
An Empirical Study and Analysis of Learning Generalizable Manipulation Skill in the SAPIEN Simulator
论文作者
论文摘要
本文简要概述了我们提交给Sapien Maniskill Challenge 2021的无互动曲目。我们的方法遵循端到端管道，主要包括两个步骤：我们首先提取多个对象的点云特征；然后，我们采用这些功能来通过基于深层变压器的网络来预测机器人模拟器的动作分数。更特别的是，为未来的工作提供指导，以开放剥削学习操纵技巧的途径，我们提出了一项经验研究，其中包括一袋技巧和流产的尝试。最后，我们的方法在排行榜上获得了有希望的排名。我们解决方案的所有代码均可在https://github.com/liu666666/bigfish \ _codes上获得。
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.
