Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction

@inproceedings{nishimura_risk-sensitive_2020,
address = {Las Vegas, NV, USA},
title = {Risk-{Sensitive} {Sequential} {Action} {Control} with {Multi}-{Modal} {Human} {Trajectory} {Forecasting} for {Safe} {Crowd}-{Robot} {Interaction}},
isbn = {978-1-72816-212-6},
url = {https://ieeexplore.ieee.org/document/9341469/},
abstract = {This paper presents a novel online framework for safe crowd-robot interaction based on risk-sensitive stochastic optimal control, wherein the risk is modeled by the entropic risk measure. The sampling-based model predictive control relies on mode insertion gradient optimization for this risk measure as well as Trajectron++, a state-of-the-art generative model that produces multimodal probabilistic trajectory forecasts for multiple interacting agents. Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control, which is advantageous compared to endto-end policy learning methods in that it allows the robot’s desired behavior to be speciﬁed at run time. In particular, we show that the robot exhibits diverse interaction behavior by varying the risk sensitivity parameter. A simulation study and a real-world experiment show that the proposed online framework can accomplish safe and efﬁcient navigation while avoiding collisions with more than 50 humans in the scene.},
language = {en},
urldate = {2021-04-09},
booktitle = {2020 {IEEE}/{RSJ} {International} {Conference} on {Intelligent} {Robots} and {Systems} ({IROS})},
publisher = {IEEE},
author = {Nishimura, Haruki and Ivanovic, Boris and Gaidon, Adrien and Pavone, Marco and Schwager, Mac},
month = oct,
year = {2020},
pages = {11205--11212},
month_numeric = {10}
}