PRoC3S

Open-source code-release for paper "Trust the PRoC3S: Solving Long-Horizon Robotics Problems with LLMs and Constraint Satisfaction".

Please reach out to Aidan Curtis (curtisa@csail.mit.edu) and Nishanth Kumar (njk@csail.mit.edu) with any questions!

Setup

conda create -n "proc3s" python=3.10
conda activate proc3s
python -m pip install -e .

Add your OpenAI Key

echo "OPENAI_KEY='<YOUR-KEY-HERE>'" > .env

Example commands

The main run file is eval_policy.py. Running a particular domain involves simply creating a config file in the vtamp/config directory and running eval_policy.py using the --config-dir . and --config_name flags.

Here are a few example commands to give you an idea:

# Our approach on a task with goal "draw a rectangle that encloses two obstacles".
python eval_policy.py --config-dir . --config-name=proc3s_draw_star.yaml

# Code as Policies on a RAVENS task with goal "Put three blocks in a line flat on the table"
python eval_policy.py --config-dir=. --config-name=cap_draw_star.yaml

# LLM^3 on a RAVENS task with goal "Put three blocks in a line flat on the table"
python eval_policy.py --config-dir=. --config-name=llm3_draw_star.yaml

To reproduce full paper experiments, see the experiments config folder here

To turn on caching for llm responses, use the +policy.use_cache=true flag. e.g.:

python eval_policy.py --config-dir=. --config-name=ours_raven.yaml +policy.use_cache=true

Finally, to visualize constraint checking, use the vis_debug=true flag. e.g.:

python eval_policy.py --config-dir=. --config-name=ours_raven.yaml vis_debug=true ++render=True