Body Transformer: Leveraging Robot Embodiment for Policy Learning
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.
66bad6168b908c4eee37a428
Carmelo Sferrazza
admin_assigned
2024-08-13T09:13:08.760Z
65ce68aec1912289cad81816
/avatars/eb9289243a533a6660e111185b012190.svg
Carmelo Sferrazza
user
csferrazza
66bad6168b908c4eee37a429
Dun-Ming Huang
66bad6168b908c4eee37a42a
Fangchen Liu
admin_assigned
2024-08-13T09:13:34.180Z
63609ef63605bd411c1c7509
/avatars/3fadc3c4117cdf17d25f3efc545388cd.svg
Fangchen Liu
user
fangchenliu
66bad6168b908c4eee37a42b
Jongmin Lee
claimed_verified
2024-08-14T08:11:41.376Z
662859a83af36f447af8f538
/avatars/2198bb2832a78c1222737ebe67d30ba3.svg
Jongmin Lee
user
secury
66bad6168b908c4eee37a42c
Pieter Abbeel
Body Transformer: Leveraging Robot Embodiment for Policy Learning
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.
The Body Transformer (BoT) architecture, which represents the robot body as a graph and uses masked attention, outperforms vanilla transformers and multilayer perceptrons in task completion, scaling, and computational efficiency for robot learning.
transformer architecture Body Transformer (BoT) robot learning inductive bias robot embodiment graph representation sensors actuators masked attention imitation learning reinforcement learning policies