AutoRT: Embodied Foundation Models for Large Scale Orchestration of
  Robotic Agents
Michael Ahn Debidatta Dwibedi Chelsea Finn Montse Gonzalez Arenas Keerthana Gopalakrishnan Karol Hausman Brian Ichter Alex Irpan Nikhil Joshi Ryan Julian Sean Kirmani Isabel Leal Edward Lee Sergey Levine Yao Lu Isabel Leal Sharath Maddineni Kanishka Rao Dorsa Sadigh Pannag Sanketi Pierre Sermanet Quan Vuong Stefan Welker Fei Xia Ted Xiao Peng Xu Steve Xu Zhuo Xu
Foundation models that incorporate language, vision, and more recently
actions have revolutionized the ability to harness internet scale data to
reason about useful tasks. However, one of the key challenges of training
embodied foundation models is the lack of data grounded in the physical world.
In this paper, we propose AutoRT, a system that leverages existing foundation
models to scale up the deployment of operational robots in completely unseen
scenarios with minimal human supervision. AutoRT leverages vision-language
models (VLMs) for scene understanding and grounding, and further uses large
language models (LLMs) for proposing diverse and novel instructions to be
performed by a fleet of robots. Guiding data collection by tapping into the
knowledge of foundation models enables AutoRT to effectively reason about
autonomy tradeoffs and safety while significantly scaling up data collection
for robot learning. We demonstrate AutoRT proposing instructions to over 20
robots across multiple buildings and collecting 77k real robot episodes via
both teleoperation and autonomous robot policies. We experimentally show that
such "in-the-wild" data collected by AutoRT is significantly more diverse, and
that AutoRT's use of LLMs allows for instruction following data collection
robots that can align to human preferences.