This paper presents an overview and comparative analysis of our systems designed for the following two tracks in SAPIEN ManiSkill Challenge 2021: No Interaction Track: The No Interaction track targets for learning policies from pre-collected demonstration trajectories. We investigate both imitation learning-based approach, i.e., imitating the observed behavior using classical supervised learning techniques, and offline reinforcement learning-based approaches, for this track. Moreover, the geometry and texture structures of objects and robotic arms are exploited via Transformer-based networks to facilitate imitation learning. No Restriction Track: In this track, we design a Heuristic Rule-based Method (HRM) to trigger high-quality object manipulation by decomposing the task into a series of sub-tasks. For each sub-task, the simple rule-based controlling strategies are adopted to predict actions that can be applied to robotic arms. To ease the implementations of our systems, all the source codes and pre-trained models are available at \url{https://github.com/caiqi/Silver-Bullet-3D/}.
@article{arxiv.2206.06289,
title = {Silver-Bullet-3D at ManiSkill 2021: Learning-from-Demonstrations and Heuristic Rule-based Methods for Object Manipulation},
author = {Yingwei Pan and Yehao Li and Yiheng Zhang and Qi Cai and Fuchen Long and Zhaofan Qiu and Ting Yao and Tao Mei},
journal= {arXiv preprint arXiv:2206.06289},
year = {2022}
}
Comments
Accepted by ICLR 2022 Workshop on Generalizable Policy Learning in Physical World. Top-performing systems for both no interaction and no restriction tracks in SAPIEN ManiSkill Challenge 2021. The source code and model are publicly available at: https://github.com/caiqi/Silver-Bullet-3D/