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Predicting Object Interactions with Behavior Primitives: An Application in Stowing Tasks

Haonan Chen, Yilong Niu, Kaiwen Hong, Shuijing Liu, Yixuan Wang, Yunzhu Li, Katherine Driggs-Campbell

CoRL 2023, Oral | Website | Paper

Demo

Real-World Demo

Our Method

Prerequisites

  • Operating System: Ubuntu 20.04.
  • Python: 3.8.
  • Environment: robosuite-1.2.

Installation Steps

  1. Create the conda environment:

    conda env create -f environment.yml
    conda activate stow
  2. Update .bashrc:

    echo 'export PYTHONPATH="${PYTHONPATH}:/path/to/stowing"' >> ~/.bashrc

Usage

Dataset Preparation for GNN

  1. Sample and Build Dataset:

    bash perception/scripts/run_sample.sh [push|sweep|insert]
  2. Manual Inspection (Optional):

    • Consolidate visualizations:
      perception/scripts/inspect_perception.sh
    • Review videos and record problematic indices in dump/perception/inspect/inspect.txt.
  3. Construct GNN Dataset:

    python perception/scripts/auto_patch_make.py

Training the Dynamics Model

You can find our pre-collected dataset here.

Execute the desired script by specifying the task (push, sweep, or insert):

python dynamics/scripts/hyperparameter_<task>.py

Code Structure

  • config/: Configuration files for perception and dynamics.
  • dynamics/: Scripts to train and evaluate the GNN.
  • perception/: The perception module of Stowing.
  • utils/: Utilities and visualization functions.

Citation

If you find our code or paper useful, please cite:

@inproceedings{
    chen2023predicting,
    title={Predicting Object Interactions with Behavior Primitives: An Application in Stowing Tasks},
    author={Haonan Chen and Yilong Niu and Kaiwen Hong and Shuijing Liu and Yixuan Wang and Yunzhu Li and Katherine Rose Driggs-Campbell},
    booktitle={7th Annual Conference on Robot Learning},
    year={2023},
    url={https://openreview.net/forum?id=VH6WIPF4Sj}       
}

Acknowledgement

The code is adapted from RoboCraft.

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