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Bayesian Relational Memory for Semantic Visual Navigation (ICCV2019)

This is the source code for our ICCV2019 paper, which implements a visual navigation agent with a Bayesian relational memory over semantic concepts in the House3D environment.

check out our paper here.

Bibtex:

@inproceedings{wu2019bayesian,
  title={Bayesian Relational Memory for Semantic Visual Navigation},
  author={Wu, Yi and Wu, Yuxin and Tamar, Aviv and Russell, Stuart and Gkioxari, Georgia and Tian, Yuandong},
  booktitle={Proceedings of the 2019 IEEE International Conference on Computer Vision (ICCV)},
  year={2019}
}

Environment

Our project developed a customized, C++ re-implementation of the House3D environment, which is much faster, consumes orders of magnitudes less memory, and provides much more APIs for task analysis and auxiliary training.

For task and environment details, please follow the original House3D paper.

PyTorch Version

The required PyTorch version is 0.3.1.

Note: the policies where trained under PyTorch 0.2.0. In order to guarantee the full reproducibility of our results, please switch to PyTorch 0.2.0 (with very tiny API changes).

It will raise run-time errors in pytorch 0.3.0. Make sure to avoid this version! The code will be kept as it is now and no further package upgrade will be performed.

Preliminary

  1. Python version 3.6.
  2. These packages are required: numpy, pytorch=0.3.1, gym, matplotlib, opencv, msgpack, msgpack_numpy.
  3. Set the House3D path properly by generating your own config.json file (see config.json.example as an example).
  4. (optional) See config.py and ensure all metadata files, all_house_ids.json (all house ids) and all_house_targets.json (semantic target types), are properly set.
  5. Stay in the root folder and ensure the following two sanity check scripts can be properly run:
    a. training test: python3 release/scripts/sanity_check/test_train.py
    b. evaluation test: python3 release/scripts/sanity_check/test_eval.py

Usage

All the scripts and trained policies are stored in release folder. To run scripts, ensure that the scripts are run from the root folder (i.e., under HouseNavAgent folder).

  1. For evaluating BRM agents and all the related baselines, check the scripts/eval folder.
  2. For re-training the polices, check the scripts/train folder.

Important Files

  1. To create an environment instance, refer to the create_env(...) function in common.py.
  2. For evaluation, refer to HRL/eval_HRL.py. Here is the reference evaluation script. See here for descriptions of all the command line options.
  3. For parallel A2C training, refer to zmq_train.py. Here is the reference training script. See here for descriptions of all the command line options.