身体变形器：利用机器人具身体性进行策略学习

Body Transformer: Leveraging Robot Embodiment for Policy Learning

August 12, 2024

作者: Carmelo Sferrazza, Dun-Ming Huang, Fangchen Liu, Jongmin Lee, Pieter Abbeel

cs.AI

摘要

近年来，Transformer架构已成为自然语言处理和计算机视觉中应用的机器学习算法的事实标准。尽管在机器人学习背景下成功部署了这种架构的显著证据，但我们认为普通Transformer并未充分利用机器人学习问题的结构。因此，我们提出了Body Transformer（BoT），这是一种利用机器人实体的架构，通过提供引导学习过程的归纳偏差。我们将机器人身体表示为传感器和执行器的图，并依赖于掩码注意力来在整个架构中汇总信息。由此产生的架构在任务完成、规模特性和计算效率方面优于普通Transformer，以及经典的多层感知器，无论是表示模仿还是强化学习策略。包括开源代码在内的其他材料可在https://sferrazza.cc/bot_site找到。

English

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.