Scaling Up and Distilling Down: Language-Guided Robot Skill Acquisition
We present a framework for robot skill acquisition, which 1) efficiently
scale up data generation of language-labelled robot data and 2) effectively
distills this data down into a robust multi-task language-conditioned
visuo-motor policy. For (1), we use a large language model (LLM) to guide
high-level planning, and sampling-based robot planners (e.g. motion or grasp
samplers) for generating diverse and rich manipulation trajectories. To
robustify this data-collection process, the LLM also infers a code-snippet for
the success condition of each task, simultaneously enabling the data-collection
process to detect failure and retry as well as the automatic labeling of
trajectories with success/failure. For (2), we extend the diffusion policy
single-task behavior-cloning approach to multi-task settings with language
conditioning. Finally, we propose a new multi-task benchmark with 18 tasks
across five domains to test long-horizon behavior, common-sense reasoning,
tool-use, and intuitive physics. We find that our distilled policy successfully
learned the robust retrying behavior in its data collection policy, while
improving absolute success rates by 34.8% on average across five domains. The
benchmark, code, and qualitative results are on our website
https://www.cs.columbia.edu/~huy/scalingup/
64c316045b7862ce872fa6a7
Huy Ha
claimed_verified
2023-07-28T16:17:00.967Z
636a8b90c95145940bfdec8c
https://cdn-avatars.huggingface.co/v1/production/uploads/1667926906903-noauth.jpeg
Huy Ha
user
huy-ha
64c316045b7862ce872fa6a8
Pete Florence
64c316045b7862ce872fa6a9
Shuran Song
Scaling Up and Distilling Down: Language-Guided Robot Skill Acquisition
We present a framework for robot skill acquisition, which 1) efficiently
scale up data generation of language-labelled robot data and 2) effectively
distills this data down into a robust multi-task language-conditioned
visuo-motor policy. For (1), we use a large language model (LLM) to guide
high-level planning, and sampling-based robot planners (e.g. motion or grasp
samplers) for generating diverse and rich manipulation trajectories. To
robustify this data-collection process, the LLM also infers a code-snippet for
the success condition of each task, simultaneously enabling the data-collection
process to detect failure and retry as well as the automatic labeling of
trajectories with success/failure. For (2), we extend the diffusion policy
single-task behavior-cloning approach to multi-task settings with language
conditioning. Finally, we propose a new multi-task benchmark with 18 tasks
across five domains to test long-horizon behavior, common-sense reasoning,
tool-use, and intuitive physics. We find that our distilled policy successfully
learned the robust retrying behavior in its data collection policy, while
improving absolute success rates by 34.8% on average across five domains. The
benchmark, code, and qualitative results are on our website
https://www.cs.columbia.edu/~huy/scalingup/
The framework scales up the generation of language-labeled robot data using a large language model and sampling-based planners, then distills it into a multi-task language-conditioned visuo-motor policy, achieving improved success rates on a new multi-task benchmark.
large language model sampling-based robot planners diffusion policy multi-task behavior-cloning multi-task benchmark language-conditioned visuo-motor policy