Body Transformer：ロボット身体性を活用した方策学習
Body Transformer: Leveraging Robot Embodiment for Policy Learning
ロボットの身体をセンサとアクチュエータのグラフとして表現し、マスク付きアテンションで情報を集約するBoTを提案。模倣学習・強化学習の方策において、通常のTransformerやMLPより高性能・高効率であることを示した。
著者: Carmelo Sferrazza, Dun-Ming Huang, Fangchen Liu, Jongmin Lee, Pieter Abbeel
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
In recent years, the transformer architecture has become the de facto standard for machine learning algorithms applied to natural language processing and computer vision. Despite notable evidence of successful deployment of this architecture in the context of robot learning, we claim that vanilla transformers do not fully exploit the structure of the robot learning problem. Therefore, we propose Body Transformer (BoT), an architecture that leverages the robot embodiment by providing an inductive bias that guides the learning process. We represent the robot body as a graph of sensors and actuators, and rely on masked attention to pool information throughout the architecture. The resulting architecture outperforms the vanilla transformer, as well as the classical multilayer perceptron, in terms of task completion, scaling properties, and computational efficiency when representing either imitation or reinforcement learning policies. Additional material including the open-source code is available at https://sferrazza.cc/bot_site.
