自由形状ロボット設計のための強化学習
Reinforcement learning for freeform robot design
原子ブロックの追加・除去により任意の外部・内部構造を持つ自由形状ロボットを設計する方策勾配法を提案した。
著者: Muhan Li, David Matthews, Sam Kriegman
分類: cs.RO, cs.AI
原文アブストラクト
Inspired by the necessity of morphological adaptation in animals, a growing body of work has attempted to expand robot training to encompass physical aspects of a robot's design. However, reinforcement learning methods capable of optimizing the 3D morphology of a robot have been restricted to reorienting or resizing the limbs of a predetermined and static topological genus. Here we show policy gradients for designing freeform robots with arbitrary external and internal structure. This is achieved through actions that deposit or remove bundles of atomic building blocks to form higher-level nonparametric macrostructures such as appendages, organs and cavities. Although results are provided for open loop control only, we discuss how this method could be adapted for closed loop control and sim2real transfer to physical machines in future.
