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Update:
[26 June 2024]: We will publish a minimal version on colab soon

An official implementation of "TTA-Nav: Test-time Adaptive Reconstruction for Point-Goal Navigation under Visual Corruptions"

Read the preprint version at: https://arxiv.org/abs/2403.01977
Project page (including demos): https://sites.google.com/view/tta-nav

Requirements

  1. Navigation agent is based on Habitat-Lab and Habitat-Baselines. Please follow the official instructions here Here we use: habitat-sim 0.3.0, habitat-lab 0.3.0, and habitat-baselines 0.3.0.

  2. Top-down decoder is independent of habitat and can be trained using standard pytorch and pytorch lighting.
    Here we adapt the code from DiffuseVAE to train our top-down decoder. Dependency: pytorch-lightning 1.4.9, torch 1.11.0 (could work with latest versions)

Description

  1. The main code for TTA-Nav is located in this folder
  2. TTA-Nav method requires minor edit of the main habitat-baseline code: modify habitat_evaluator.py. because it adapts during the test time.

Train Top-down Deocder

First of all, we have to prepare an image dataset by running habitat evaluation with pre-defined episodes. Before running this, we have to download Gibson Scene dataset from the official website. Please see Datasets for the guidelines. Our work uses v1 dataset.

Then, we can run the train and test scripts.

Training Script: train_ae.sh

bash scripts/train_ae.sh

Test Script: test_ae.sh

bash scripts/test_ae.sh

Run TTA-Nav Agent

Our method is tested on Habitat-lab. Please check the official website for installation. The code below is to run TTA-Nav agent under Lighting corruption. Other corruptions can be run with a similar command but changing .yaml files.

python -u -m habitat_baselines.run habitat_baselines.evaluate=True --config-name pointnav/tta-nav/adapt/lighting_5.yaml

We only have to modify habitat_evaluator.py and add ae folder to the ppo folder.

Checkpoints

  1. The main agent: DD-PPO (BatchNorm version)
  2. Top-down Decoder: ae_gibson.pt

Contact

Any inquiry can be made via maytusp [at] gmail [dot] com

Cite

If you find our project useful, please cite us by

@article{piriyajitakonkij2024tta,
  title={TTA-Nav: Test-time Adaptive Reconstruction for Point-Goal Navigation under Visual Corruptions},
  author={Piriyajitakonkij, Maytus and Sun, Mingfei and Zhang, Mengmi and Pan, Wei},
  journal={arXiv preprint arXiv:2403.01977},
  year={2024}
}