Gazebo_CrowdNav

This repository contains the codes for Gazebo simulations with Crowd Navigation SOTAs with ROS1 support. pc2obs.py read pointcloud from depth camera and subsamples it to voxels of obstacles.

This repository contains the codes for our ICRA 2019 paper. For more details, please refer to the paper Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning.

Please find our more recent work on

• Relational Graph Learning for Crowd Navigation.
• Social NCE: Contrastive Learning of Socially-aware Motion Representations.

Setup

1. Install Python-RVO2 library. (Must build the repository in this link, there are additional functions)
2. Install crowd_sim and crowd_nav into pip

pip install -e .

Getting Started

This repository is organized in two parts: gym_crowd/ folder contains the simulation environment and crowd_nav/ folder contains codes for training and testing the policies. Details of the simulation framework can be found here. Below are the instructions for training and testing policies, and they should be executed inside the crowd_nav/ folder.

1. Train a policy.

python train.py --policy sarl

2. Test policies with 500 test cases.

python test.py --policy orca --phase test
python test.py --policy sarl --model_dir data/output --phase test

3. Run policy for one episode and visualize the result.

python test.py --policy orca --phase test --visualize --test_case 0
python test.py --policy sarl --model_dir data/output --phase test --visualize --test_case 0

4. Visualize a test case.

python test.py --policy sarl --model_dir data/output --phase test --visualize --test_case 0

5. Plot training curve.

python utils/plot.py data/output/output.log

Simulation Videos

CADRL	LSTM-RL
SARL	OM-SARL

Learning Curve

Learning curve comparison between different methods in an invisible setting.

Citation

If you find the codes or paper useful for your research, please cite our paper:

@inproceedings{chen2019crowd,
  title={Crowd-robot interaction: Crowd-aware robot navigation with attention-based deep reinforcement learning},
  author={Chen, Changan and Liu, Yuejiang and Kreiss, Sven and Alahi, Alexandre},
  booktitle={2019 International Conference on Robotics and Automation (ICRA)},
  pages={6015--6022},
  year={2019},
  organization={IEEE}
}