title: Body Transformer: Leveraging Robot Embodiment for Policy Learning
summary: 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.
paper_authors._id: 66bad6168b908c4eee37a428
paper_authors.name: Carmelo Sferrazza
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-13T09:13:08.760Z
paper_authors._id: 65ce68aec1912289cad81816
paper_authors.avatarUrl: /avatars/eb9289243a533a6660e111185b012190.svg
paper_authors.fullname: Carmelo Sferrazza
paper_authors.type: user
paper_authors.user: csferrazza
paper_authors._id: 66bad6168b908c4eee37a429
paper_authors.name: Dun-Ming Huang
paper_authors._id: 66bad6168b908c4eee37a42a
paper_authors.name: Fangchen Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-13T09:13:34.180Z
paper_authors._id: 63609ef63605bd411c1c7509
paper_authors.avatarUrl: /avatars/3fadc3c4117cdf17d25f3efc545388cd.svg
paper_authors.fullname: Fangchen Liu
paper_authors.type: user
paper_authors.user: fangchenliu
paper_authors._id: 66bad6168b908c4eee37a42b
paper_authors.name: Jongmin Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-14T08:11:41.376Z
paper_authors._id: 662859a83af36f447af8f538
paper_authors.avatarUrl: /avatars/2198bb2832a78c1222737ebe67d30ba3.svg
paper_authors.fullname: Jongmin Lee
paper_authors.type: user
paper_authors.user: secury
paper_authors._id: 66bad6168b908c4eee37a42c
paper_authors.name: Pieter Abbeel
paper_title: Body Transformer: Leveraging Robot Embodiment for Policy Learning
paper_summary: 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.
paper_ai_keywords: transformer architecture Body Transformer (BoT) inductive bias graph of sensors and actuators masked attention multilayer perceptron imitation learning reinforcement learning task completion scaling properties computational efficiency