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

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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

![example](./docs/example.gif)

### 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.

![overview](./docs/overview.png)

## 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.