3D点群強化学習の有効性検証
On the Efficacy of 3D Point Cloud Reinforcement Learning
3D点群を用いた強化学習の設計選択を体系的に検討し、エージェント-物体や物体間の関係推論が重要なタスクで2D画像より優れることを示した。
著者: Zhan Ling, Yunchao Yao, Xuanlin Li, Hao Su
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Recent studies on visual reinforcement learning (visual RL) have explored the use of 3D visual representations. However, none of these work has systematically compared the efficacy of 3D representations with 2D representations across different tasks, nor have they analyzed 3D representations from the perspective of agent-object / object-object relationship reasoning. In this work, we seek answers to the question of when and how do 3D neural networks that learn features in the 3D-native space provide a beneficial inductive bias for visual RL. We specifically focus on 3D point clouds, one of the most common forms of 3D representations. We systematically investigate design choices for 3D point cloud RL, leading to the development of a robust algorithm for various robotic manipulation and control tasks. Furthermore, through comparisons between 2D image vs 3D point cloud RL methods on both minimalist synthetic tasks and complex robotic manipulation tasks, we find that 3D point cloud RL can significantly outperform the 2D counterpart when agent-object / object-object relationship encoding is a key factor.
