Object Goal Navigation using Goal-Oriented Semantic Exploration

This is a PyTorch implementation of the NeurIPS-20 paper:

Object Goal Navigation using Goal-Oriented Semantic Exploration
Devendra Singh Chaplot, Dhiraj Gandhi, Abhinav Gupta, Ruslan Salakhutdinov
Carnegie Mellon University, Facebook AI Research

Winner of the CVPR 2020 Habitat ObjectNav Challenge.

Project Website: https://devendrachaplot.github.io/projects/semantic-exploration

Overview:

The Goal-Oriented Semantic Exploration (SemExp) model consists of three modules: a Semantic Mapping Module, a Goal-Oriented Semantic Policy, and a deterministic Local Policy. As shown below, the Semantic Mapping model builds a semantic map over time. The Goal-Oriented Semantic Policy selects a long-term goal based on the semantic map to reach the given object goal efficiently. A deterministic local policy based on analytical planners is used to take low-level navigation actions to reach the long-term goal.

This repository contains:

Episode train and test datasets for Object Goal Navigation task for the Gibson dataset in the Habitat Simulator.
The code to train and evaluate the Semantic Exploration (SemExp) model on the Object Goal Navigation task.
Pretrained SemExp model.

Installing Dependencies

We use earlier versions of habitat-sim and habitat-lab as specified below:

Installing habitat-sim:

git clone https://github.com/facebookresearch/habitat-sim.git
cd habitat-sim; git checkout tags/v0.1.5; 
pip install -r requirements.txt; 
python setup.py install --headless
python setup.py install # (for Mac OS)

Installing habitat-lab:

git clone https://github.com/facebookresearch/habitat-lab.git
cd habitat-lab; git checkout tags/v0.1.5; 
pip install -e .

Check habitat installation by running python examples/benchmark.py in the habitat-lab folder.

Install pytorch according to your system configuration. The code is tested on pytorch v1.6.0 and cudatoolkit v10.2. If you are using conda:

conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 #(Linux with GPU)
conda install pytorch==1.6.0 torchvision==0.7.0 -c pytorch #(Mac OS)

Install detectron2 according to your system configuration. If you are using conda:

python -m pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.6/index.html #(Linux with GPU)
CC=clang CXX=clang++ ARCHFLAGS="-arch x86_64" python -m pip install 'git+https://github.com/facebookresearch/detectron2.git' #(Mac OS)

Docker and Singularity images:

We provide experimental docker and singularity images with all the dependencies installed, see Docker Instructions.

Setup

Clone the repository and install other requirements:

git clone https://github.com/devendrachaplot/Object-Goal-Navigation/
cd Object-Goal-Navigation/;
pip install -r requirements.txt

Downloading scene dataset

Download the Gibson dataset using the instructions here: https://github.com/facebookresearch/habitat-lab#scenes-datasets (download the 11GB file gibson_habitat_trainval.zip)
Move the Gibson scene dataset or create a symlink at data/scene_datasets/gibson_semantic.

Downloading episode dataset

Download the episode dataset:

wget --no-check-certificate 'https://drive.google.com/uc?export=download&id=1tslnZAkH8m3V5nP8pbtBmaR2XEfr8Rau' -O objectnav_gibson_v1.1.zip

Unzip the dataset into data/datasets/objectnav/gibson/v1.1/

Setting up datasets

The code requires the datasets in a data folder in the following format (same as habitat-lab):

Object-Goal-Navigation/
  data/
    scene_datasets/
      gibson_semantic/
        Adrian.glb
        Adrian.navmesh
        ...
    datasets/
      objectnav/
        gibson/
          v1.1/
            train/
            val/

Test setup

To verify that the data is setup correctly, run:

python test.py --agent random -n1 --num_eval_episodes 1 --auto_gpu_config 0

Usage

Training:

For training the SemExp model on the Object Goal Navigation task:

python main.py

Downloading pre-trained models

mkdir pretrained_models;
wget --no-check-certificate 'https://drive.google.com/uc?export=download&id=171ZA7XNu5vi3XLpuKs8DuGGZrYyuSjL0' -O pretrained_models/sem_exp.pth

For evaluation:

For evaluating the pre-trained model:

python main.py --split val --eval 1 --load pretrained_models/sem_exp.pth

For visualizing the agent observations and predicted semantic map, add -v 1 as an argument to the above command.

The pre-trained model should get 0.657 Success, 0.339 SPL and 1.474 DTG.

For more detailed instructions, see INSTRUCTIONS.

Cite as

Chaplot, D.S., Gandhi, D., Gupta, A. and Salakhutdinov, R., 2020. Object Goal Navigation using Goal-Oriented Semantic Exploration. In Neural Information Processing Systems (NeurIPS-20). (PDF)

Bibtex:

@inproceedings{chaplot2020object,
  title={Object Goal Navigation using Goal-Oriented Semantic Exploration},
  author={Chaplot, Devendra Singh and Gandhi, Dhiraj and
            Gupta, Abhinav and Salakhutdinov, Ruslan},
  booktitle={In Neural Information Processing Systems (NeurIPS)},
  year={2020}
  }

Related Projects

This project builds on the Active Neural SLAM paper. The code and pretrained models for the Active Neural SLAM system are available at: https://github.com/devendrachaplot/Neural-SLAM.
The Semantic Mapping module is similar to the one used in Semantic Curiosity.

Acknowledgements

This repository uses Habitat Lab implementation for running the RL environment. The implementation of PPO is borrowed from ikostrikov/pytorch-a2c-ppo-acktr-gail. The Mask-RCNN implementation is based on the detectron2 repository. We would also like to thank Shubham Tulsiani and Saurabh Gupta for their help in implementing some parts of the code.

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object-goal-navigation's Introduction