PointPatchRL：マスク付き再構成による点群強化学習の改善
PointPatchRL -- Masked Reconstruction Improves Reinforcement Learning on Point Clouds
点群をパッチ分割してトークン化しTransformerで処理する強化学習手法を提案し、マスク付き再構成による表現学習を組み合わせることで、変形物体を含む複雑な操作タスクで画像ベースの手法を上回る性能を示した。
著者: Balázs Gyenes, Nikolai Franke, Philipp Becker, Gerhard Neumann
分類: cs.LG, cs.RO
原文アブストラクト
Perceiving the environment via cameras is crucial for Reinforcement Learning (RL) in robotics. While images are a convenient form of representation, they often complicate extracting important geometric details, especially with varying geometries or deformable objects. In contrast, point clouds naturally represent this geometry and easily integrate color and positional data from multiple camera views. However, while deep learning on point clouds has seen many recent successes, RL on point clouds is under-researched, with only the simplest encoder architecture considered in the literature. We introduce PointPatchRL (PPRL), a method for RL on point clouds that builds on the common paradigm of dividing point clouds into overlapping patches, tokenizing them, and processing the tokens with transformers. PPRL provides significant improvements compared with other point-cloud processing architectures previously used for RL. We then complement PPRL with masked reconstruction for representation learning and show that our method outperforms strong model-free and model-based baselines on image observations in complex manipulation tasks containing deformable objects and variations in target object geometry. Videos and code are available at https://alrhub.github.io/pprl-website
