/Statler

[ICRA 24] Statler: State-Maintaining Language Models for Embodied Reasoning

Primary LanguagePython

Statler: State-Maintaining Language Models for Embodied Reasoning

Takuma Yoneda*, Jiading Fang*, Peng Li*, Huanyu Zhang*,
Tianchong Jiang, Shengjie Lin, Ben Picker, David Yunis, Hongyuan Mei, Matthew R. Walter

[Paper] [Website]

statler_teaser

Set up your environment

$ pip install -e .

If things fail, you can look at pyproject.toml to find dependencies. (If you use pdm, you should be able to just run pdm install)

Then, please set your OPENAI_API_KEY:

$ export OPENAI_API_KEY='sk-xxxxxxxxxxxxxxx'

What you can play with

Running a minimal demo

$ python -m cap.minimal_demo

This runs Statler on a simple environment with covers and objects.

Running models on evaluation episodes

$ python -m cap.experiments.evaluate_models disinfection --agents baseline

This runs baseline agent on each episode in disinfection domain, and save the results to

  • results/{task_name}/baseline-episode{ep_idx}.txt

Directories

  • cap/experiments/prompts

    • In-context learning prompt for each domain (pick_and_place, disinfection, weight, real_robot)
    • There are 3 prompts for each domain
      • cap_baseline: used by the baseline CaP agent
      • cap_wm_reader: used by Statler, world state reader
      • cap_wm_updater: used by Statler, world state writer
    • cap_auto_* is for Statler-Auto

  • cap/experiments/eval_prompts

    • User queries and expected code for each domain, used in our evaluation
    • Please disregard "gold_next_state" entry

  • results-reference

    • generated code / state during evaluation for each domain

Disclaimer

Some of the code here were used to run real robot experiments, which contains a lot of low-level functions (for example, to identify the bounding box as well as orientation of an object from its pointcloud). Although it's difficult for us to provide a clean and complete code repository that works for other UR5s out-of-the box, we leave them here in case they're helpful.

If you find our work useful in your research, please consider citing the paper as follows:

@inproceedings{yoneda2024statler,
  title={Statler: State-Maintaining Language Models for Embodied Reasoning}, 
  author={Takuma Yoneda and Jiading Fang and Peng Li and Huanyu Zhang and 
  Tianchong Jiang and Shengjie Lin and Ben Picker and David Yunis and Hongyuan Mei and Matthew R. Walter},
  booktitle={Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
  year={2024},
}