Active Uncertainty Learning for Human-Robot Interaction: An Implicit Dual Control Approach
We provide an MATLAB implementation of implicit dual control-based active uncertainty learning for autonomous driving applications, which can be found here.
Click to watch our spotlight video:
MPT3
(Toolbox for MPC and parametric optimization.)SNOPT
(Nonlinear programming solver. Academic licenses are available.)
Robotics Toolbox for MATLAB
(Tools for plotting the vehicles. You need to first install it in MATLAB and then replaceplot_vehicle.m
in the root directory with ours.)
In this repository, we provide an example of our method applied for human-robot interactive driving scenarios.
- Clone the repo
git clone https://github.com/SafeRoboticsLab/Dual_Control_HRI.git
- In MATLAB, run the
main.m
script to reproduce our results.
Distributed under the BSD 3-Clause License. See LICENSE
for more information.
Haimin Hu - @HaiminHu - haiminh@princeton.edu
Project Link: https://github.com/SafeRoboticsLab/Dual_Control_HRI
Homepage Link: https://haiminhu.org/dual_control_hri
Available on arXiv: https://arxiv.org/abs/2202.07720
@incollection{hu2022active,
title={Active Uncertainty Reduction for Human-Robot Interaction: An Implicit Dual Control Approach},
author={Hu, Haimin and Fisac, Jaime F.},
booktitle={Algorithmic Foundations of Robotics XV},
year={2022},
publisher={Springer}
}
Our prior work:
@article{hu2022sharp,
author={Hu, Haimin and Nakamura, Kensuke and Fisac, Jaime F.},
journal={IEEE Robotics and Automation Letters},
title={SHARP: Shielding-Aware Robust Planning for Safe and Efficient Human-Robot Interaction},
year={2022},
volume={7},
number={2},
pages={5591-5598},
doi={10.1109/LRA.2022.3155229}
}