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https://github.com/devendrachaplot/Neural-SLAM
Pytorch code for ICLR-20 Paper "Learning to Explore using Active Neural SLAM"
https://github.com/devendrachaplot/Neural-SLAM
active-neural-slam deep-learning deep-reinforcement-learning deep-rl exploration navigation neural-slam robotics visual-navigation
Last synced: 27 days ago
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Pytorch code for ICLR-20 Paper "Learning to Explore using Active Neural SLAM"
- Host: GitHub
- URL: https://github.com/devendrachaplot/Neural-SLAM
- Owner: devendrachaplot
- License: mit
- Created: 2020-04-10T20:03:45.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2022-12-15T06:29:53.000Z (over 1 year ago)
- Last Synced: 2024-02-10T05:05:36.488Z (4 months ago)
- Topics: active-neural-slam, deep-learning, deep-reinforcement-learning, deep-rl, exploration, navigation, neural-slam, robotics, visual-navigation
- Language: Python
- Homepage: http://www.cs.cmu.edu/~dchaplot/projects/neural-slam.html
- Size: 6 MB
- Stars: 688
- Watchers: 23
- Forks: 133
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- Awesome-SLAM - Active Neural SLAM
README
# Active Neural SLAM
This is a PyTorch implementation of the ICLR-20 paper:
[Learning To Explore Using Active Neural SLAM](https://openreview.net/pdf?id=HklXn1BKDH)
Devendra Singh Chaplot, Dhiraj Gandhi, Saurabh Gupta, Abhinav Gupta, Ruslan Salakhutdinov
Carnegie Mellon University, Facebook AI Research, UIUC
Project Website: https://devendrachaplot.github.io/projects/Neural-SLAM
### Overview:
The Active Neural SLAM model consists of three modules: a Global Policy, a Local Policy and a Neural SLAM Module.
As shown below, the Neural-SLAM module predicts a map and agent pose estimate from incoming RGB observations and
sensor readings. This map and pose are used by a Global policy to output a long-term goal, which is converted to
a short-term goal using an analytic path planner. A Local Policy is trained to navigate to this short-term goal.
## Installing Dependencies
We use earlier versions of [habitat-sim](https://github.com/facebookresearch/habitat-sim) and [habitat-api](https://github.com/facebookresearch/habitat-api). The specific commits are mentioned below.
Installing habitat-sim:
```
git clone https://github.com/facebookresearch/habitat-sim.git
cd habitat-sim; git checkout 9575dcd45fe6f55d2a44043833af08972a7895a9;
pip install -r requirements.txt;
python setup.py install --headless
python setup.py install # (for Mac OS)
```
Installing habitat-api:
```
git clone https://github.com/facebookresearch/habitat-api.git
cd habitat-api; git checkout b5f2b00a25627ecb52b43b13ea96b05998d9a121;
pip install -e .
```
Install pytorch from https://pytorch.org/ according to your system configuration. The code is tested on pytorch v1.2.0. If you are using conda:
```
conda install pytorch==1.2.0 torchvision cudatoolkit=10.0 -c pytorch #(Linux with GPU)
conda install pytorch==1.2.0 torchvision==0.4.0 -c pytorch #(Mac OS)
```
## Setup
Clone the repository and install other requirements:
```
git clone --recurse-submodules https://github.com/devendrachaplot/Neural-SLAM
cd Neural-SLAM;
pip install -r requirements.txt
```
The code requires datasets in a `data` folder in the following format (same as habitat-api):
```
Neural-SLAM/
data/
scene_datasets/
gibson/
Adrian.glb
Adrian.navmesh
...
datasets/
pointnav/
gibson/
v1/
train/
val/
...
```
Please download the data using the instructions here: https://github.com/facebookresearch/habitat-api#data
To verify that dependencies are correctly installed and data is setup correctly, run:
```
python main.py -n1 --auto_gpu_config 0 --split val
```
## Usage
### Training:
For training the complete Active Neural SLAM model on the Exploration task:
```
python main.py
```
### Downloading pre-trained models
```
mkdir pretrained_models;
wget --no-check-certificate 'https://drive.google.com/uc?export=download&id=1UK2hT0GWzoTaVR5lAI6i8o27tqEmYeyY' -O pretrained_models/model_best.global;
wget --no-check-certificate 'https://drive.google.com/uc?export=download&id=1A1s_HNnbpvdYBUAiw2y1JmmELRLfAJb8' -O pretrained_models/model_best.local;
wget --no-check-certificate 'https://drive.google.com/uc?export=download&id=1o5OG7DIUKZyvi5stozSqRpAEae1F2BmX' -O pretrained_models/model_best.slam;
```
### For evaluation:
For evaluating the pre-trained models:
```
python main.py --split val --eval 1 --train_global 0 --train_local 0 --train_slam 0 \
--load_global pretrained_models/model_best.global \
--load_local pretrained_models/model_best.local \
--load_slam pretrained_models/model_best.slam
```
For visualizing the agent observations and predicted map and pose, add `-v 1` as an argument to the above
For more detailed instructions, see [INSTRUCTIONS](./docs/INSTRUCTIONS.md).
## Cite as
>Chaplot, D.S., Gandhi, D., Gupta, S., Gupta, A. and Salakhutdinov, R., 2020. Learning To Explore Using Active Neural SLAM. In International Conference on Learning Representations (ICLR). ([PDF](https://openreview.net/pdf?id=HklXn1BKDH))
### Bibtex:
```
@inproceedings{chaplot2020learning,
title={Learning To Explore Using Active Neural SLAM},
author={Chaplot, Devendra Singh and Gandhi, Dhiraj and Gupta, Saurabh and Gupta, Abhinav and Salakhutdinov, Ruslan},
booktitle={International Conference on Learning Representations (ICLR)},
year={2020}
}
```
## Acknowledgements
This repository uses Habitat API (https://github.com/facebookresearch/habitat-api) and parts of the code from the API.
The implementation of PPO is borrowed from https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail/.
We thank Guillaume Lample for discussions and coding during initial stages of this project.