  Collecting large amounts of real-world interaction data to train general
robotic policies is often prohibitively expensive, thus motivating the use of
simulation data. However, existing methods for data generation have generally
focused on scene-level diversity (e.g., object instances and poses) rather than
task-level diversity, due to the human effort required to come up with and
verify novel tasks. This has made it challenging for policies trained on
simulation data to demonstrate significant task-level generalization. In this
paper, we propose to automatically generate rich simulation environments and
expert demonstrations by exploiting a large language models' (LLM) grounding
and coding ability. Our approach, dubbed GenSim, has two modes: goal-directed
generation, wherein a target task is given to the LLM and the LLM proposes a
task curriculum to solve the target task, and exploratory generation, wherein
the LLM bootstraps from previous tasks and iteratively proposes novel tasks
that would be helpful in solving more complex tasks. We use GPT4 to expand the
existing benchmark by ten times to over 100 tasks, on which we conduct
supervised finetuning and evaluate several LLMs including finetuned GPTs and
Code Llama on code generation for robotic simulation tasks. Furthermore, we
observe that LLMs-generated simulation programs can enhance task-level
generalization significantly when used for multitask policy training. We
further find that with minimal sim-to-real adaptation, the multitask policies
pretrained on GPT4-generated simulation tasks exhibit stronger transfer to
unseen long-horizon tasks in the real world and outperform baselines by 25%.
See the project website (https://liruiw.github.io/gensim) for code, demos, and
videos.
