Topological Memory Nav

This is an official GitHub Repository for paper "Cognitive Navigation for Intelligent Mobile Robots: A Learning-Based Approach with Topological Memory Configuration", which is accepted in IEEE/CAA Journal of Automatica Sinica.

Setup

Requirements

The source code is tested in the following setting.

• Python 3.7
• pytorch 1.12
• habitat-sim 0.2.1
• habitat-lab 0.2.1

Please refer to habitat-sim and habitat-lab for installation.

Habitat Dataset (Gibson, MP3D) Setup

The recommended folder structure of habitat-lab:

habitat-lab
    └──habitat
        └── data
            └── datasets
            │   └── pointnav
            │       └── gibson
            │           └── v1
            │               └── train
            │               └── val
            └── scene_datasets
                └── gibson_habitat
                    └── *.glb, *.navmeshs  

How to run

1. Data generation

   python collect_IL_data.py --ep-per-env 200 --num-procs 4 --split train --data-dir /path/to/save/data
   

   This will generate the data for imitation learning.

2. Training

   python train_bc.py --config configs/ltm.yaml --stop --gpu 0 --data-dir /path/to/save/data
   

3. Evaluation

   python evaluate_random.py --config configs/ltm.yaml --version-name test --eval-ckpt your_model_ckpt.pt --stop --diff random