  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 procedure, while
improving absolute success rates by 33.2% on average across five domains. Code,
data, and additional qualitative results are available on
https://www.cs.columbia.edu/~huy/scalingup/.
