This repository contains the codes for our paper titled "Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph" in ICRA 2023. For more details, please refer to the project website and arXiv preprint. For experiment demonstrations, please refer to the youtube video.

[News]

• Please check out our open-sourced sim2real tutorial here

We study the problem of safe and intention-aware robot navigation in dense and interactive crowds. Most previous reinforcement learning (RL) based methods fail to consider different types of interactions among all agents or ignore the intentions of people, which results in performance degradation. In this paper, we propose a novel recurrent graph neural network with attention mechanisms to capture heterogeneous interactions among agents through space and time. To encourage longsighted robot behaviors, we infer the intentions of dynamic agents by predicting their future trajectories for several timesteps. The predictions are incorporated into a model-free RL framework to prevent the robot from intruding into the intended paths of other agents. We demonstrate that our method enables the robot to achieve good navigation performance and non-invasiveness in challenging crowd navigation scenarios. We successfully transfer the policy learned in simulation to a real-world TurtleBot 2i.

1. In a conda environment or virtual environment with Python 3.x, install the required python package

pip install -r requirements.txt

2. Install Pytorch 1.12.1 following the instructions here

3. Install OpenAI Baselines

git clone https://github.com/openai/baselines.git
cd baselines
pip install -e .

4. Install Python-RVO2 library

This repository is organized in five parts:

• crowd_nav/ folder contains configurations and policies used in the simulator.
• crowd_sim/ folder contains the simulation environment.
• gst_updated/ folder contains the code for running inference of a human trajectory predictor, named Gumbel Social Transformer (GST) [2].
• rl/ contains the code for the RL policy networks, wrappers for the prediction network, and ppo algorithm.
• trained_models/ contains some pretrained models provided by us.

Note that this repository does not include codes for training a trajectory prediction network. Please refer to from this repo instead.

• Modify the configurations.

  1. Environment configurations: Modify crowd_nav/configs/config.py. Especially,

     • Choice of human trajectory predictor:
       • Set sim.predict_method = 'inferred' if a learning-based GST predictor is used [2]. Please also change pred.model_dir to be the directory of a trained GST model. We provide two pretrained models here.
       • Set sim.predict_method = 'const_vel' if constant velocity model is used.
       • Set sim.predict_method = 'truth' if ground truth predictor is used.
       • Set sim.predict_method = 'none' if you do not want to use future trajectories to change the observation and reward.
     • Randomization of human behaviors: If you want to randomize the ORCA humans,
       • set env.randomize_attributes to True to randomize the preferred velocity and radius of humans;
       • set humans.random_goal_changing to True to let humans randomly change goals before they arrive at their original goals.

  2. PPO and network configurations: modify arguments.py

     • env_name (must be consistent with sim.predict_method in crowd_nav/configs/config.py):
       • If you use the GST predictor, set to CrowdSimPredRealGST-v0.
       • If you use the ground truth predictor or constant velocity predictor, set to CrowdSimPred-v0.
       • If you don't want to use prediction, set to CrowdSimVarNum-v0.
     • use_self_attn: human-human attention network will be included if set to True, else there will be no human-human attention.
     • use_hr_attn: robot-human attention network will be included if set to True, else there will be no robot-human attention.
• After you change the configurations, run

  python train.py 
  

• The checkpoints and configuration files will be saved to the folder specified by output_dir in arguments.py.

Please modify the test arguments in line 20-33 of test.py (Don't set the argument values in terminal!), and run

python test.py 

Note that the config.py and arguments.py in the testing folder will be loaded, instead of those in the root directory.
The testing results are logged in trained_models/your_output_dir/test/ folder, and are also printed on terminal.
If you set visualize=True in test.py, you will be able to see visualizations like this:

To visualize the episodes with predicted human trajectories, as well as saving visualizations to disk, please refer to save_slides branch.
Note that the above visualization and file saving will slow down testing significantly!

• Set save_slides=True in test.py and all rendered frames will be saved in a subfolder inside the trained_models/your_output_dir/social_eval/.

python plot.py

Here are example learning curves of our proposed network model with GST predictor.

We are happy to announce that our sim2real tutorial and code are released here!
Note: This repo only serves as a reference point for the sim2real transfer of crowd navigation. Since there are lots of uncertainties in real-world experiments that may affect performance, we cannot guarantee that it is reproducible on all cases.

1. We only tested our code in Ubuntu with Python 3.6 and Python 3.8. The code may work on other OS or other versions of Python, but we do not have any guarantee.

2. The performance of our code can vary depending on the choice of hyperparameters and random seeds (see this reddit post). Unfortunately, we do not have time or resources for a thorough hyperparameter search. Thus, if your results are slightly worse than what is claimed in the paper, it is normal. To achieve the best performance, we recommend some manual hyperparameter tuning.

If you find the code or the paper useful for your research, please cite the following papers:

@inproceedings{liu2022intention,
  title={Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph},
  author={Liu, Shuijing and Chang, Peixin and Huang, Zhe and Chakraborty, Neeloy and Hong, Kaiwen and Liang, Weihang and Livingston McPherson, D. and Geng, Junyi and Driggs-Campbell, Katherine},
  booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
  year={2023}
}

@inproceedings{liu2020decentralized,
  title={Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning},
  author={Liu, Shuijing and Chang, Peixin and Liang, Weihang and Chakraborty, Neeloy and Driggs-Campbell, Katherine},
  booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
  year={2021},
  pages={3517-3524}
}

Other contributors:
Peixin Chang
Zhe Huang
Neeloy Chakraborty

Part of the code is based on the following repositories:

[1] S. Liu, P. Chang, W. Liang, N. Chakraborty, and K. Driggs-Campbell, "Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning," in IEEE International Conference on Robotics and Automation (ICRA), 2019, pp. 3517-3524. (Github: https://github.com/Shuijing725/CrowdNav_DSRNN)

[2] Z. Huang, R. Li, K. Shin, and K. Driggs-Campbell. "Learning Sparse Interaction Graphs of Partially Detected Pedestrians for Trajectory Prediction," in IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 1198–1205, 2022. (Github: https://github.com/tedhuang96/gst)

If you have any questions or find any bugs, please feel free to open an issue or pull request.