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DeftEnv: A Simulated Environment for Sequential Manipulation

This is a reference implementation for the environments used in Deep Affordance Foresight. If you find this codebase useful, please cite the work using the following bibtex:

@inproceedings{xu2021deep,
  title={Deep affordance foresight: Planning through what can be done in the future},
  author={Xu, Danfei and Mandlekar, Ajay and Mart{\'\i}n-Mart{\'\i}n, Roberto and Zhu, Yuke and Savarese, Silvio and Fei-Fei, Li},
  booktitle={2021 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={6206--6213},
  year={2021},
  organization={IEEE}
}

Getting Started

1. Install DeftEnv:

$ git clone https://github.com/danfeiX/deftenv.git
$ cd deftenv
# you may want to create a separate virtualenv to avoid package contamination
$ pip install -e .

2. Run an example

Run and visualize a guided exploration session and collect demonstrations.

Tool manipulation + stacking:

$ python scripts/kitchen_main.py --mode demo --env ToolStackAP --file demo.hdf5 --gui --keep_failed_demos --keep_interrupted_demos

Kitchen make coffee or tea (random sample task goal):

$ python scripts/kitchen_main.py --mode demo --env KitchenDualCoffeeAP --file demo.hdf5 --gui --keep_failed_demos --keep_interrupted_demos

3. Extract training data

Extract training data from the demonstrations that we just collected in demo.hdf5

python deftenv/scripts/kitchen_main.py --mode extract_skill --file demo.hdf5 --extract_name training_data.hdf5

Acknowledgement

  • The kitchen environment assets are created by William Shen.
  • The pybullet tools are borrowed from Caelan's awesome PyBullet Planning codebase.