Arxiv Fleet-Merge Website Policy-Composition Website
This repo is the simulation environment benchmark built with Gym API and Drake simulations for Franka Panda. The repository features tool-use tasks with scripted experts.
pip install -r requirements
- These commands will use the hand-scripted joint-space kpam planner to plan for demonstration trajectories
- Run
bash experiments/running_scripts/frankahammer_tool_datacollection_test_rgb.sh. openlocal_host:6006 - Run
bash experiments/running_scripts/**_datacollection.shto generate the data. And export to the global python path such that the training repofleet_diffusioncan import it and run evaluation.
- Run
python -m core.run run_expert=True teleop=True teleop_type=keyboard expert=HumanExpert task=FrankaDrakeSpatulaEnv task.use_meshcat=True. openlocal_host:6006to use keyboardwsadto teleop. - If you want to try Oculus Quest >=2. Run:
python -m core.run run_expert=True teleop=True teleop_type=vr expert=HumanExpert task=FrankaDrakeSpatulaEnv task.use_meshcat=TrueHold the trigger for moving.
├── ...
├── Fleet-Tools
| |── assets # object assets
| |── core # source code
| | |── agent # replay buffer to save data offline
| | |── expert # kpam expert to generate demonstrations
| | └── ...
| |── env # environment code
| |── scripts # data preprocessing
| |── experiments # data generation scripts and task configs
└── ...
- Code Quality Level: Tired grad student.
- Have Questions: Open Github issues or send an email.
MIT
- Kpam and manipulation.
- Assets are attributed to a mix of Mcmaster and Objaverse with CC-BY License.
If you find Fleet-Tools useful in your research, please consider citing:
@inproceedings{wang2023fleet,
author = {Lirui Wang, Kaiqing Zhang, Allan Zhou, Max Simchowitz, Russ Tedrake},
title = {Fleet Policy Learning Via Weight Mering and An Application to Tool Use},
booktitle = {Arxiv},
year = {2023}
}