TY - GEN
T1 - Stackelberg Strategic Guidance for Heterogeneous Robots Collaboration
AU - Zhao, Yuhan
AU - Huang, Baichuan
AU - Yu, Jingjin
AU - Zhu, Quanyan
N1 - Funding Information:
1Y. Zhao and Q. Zhu are with the Department of Electrical and Computer Engineering, New York University. Email: {yhzhao, qz494}@nyu.edu. 2B. Huang and J. Yu are with the Department of Computer Science, Rutgers University. Email: {baichuan.huang, jingjin.yu}@rutgers.edu. This work is supported by NSF awards IIS-1845888 and IIS-2132972; partially supported by grants ECCS-1847056 from NSF and grant W911NF-19-1-0041 from Army Research Office (ARO).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this study, we explore the application of game theory, in particular Stackelberg games, to address the issue of effective coordination strategy generation for heterogeneous robots with one-way communication. To that end, focusing on the task of multi-object rearrangement, we develop a theoretical and algorithmic framework that provides strategic guidance for a pair of robot arms, a leader and a follower where the leader has a model of the follower's decision-making process, through the computation of a feedback Stackelberg equilibrium. With built-in tolerance of model uncertainty, the strategic guidance generated by our planning algorithm not only improves the overall efficiency in solving the rearrangement tasks, but is also robust to common pitfalls in collaboration, e.g., chattering.
AB - In this study, we explore the application of game theory, in particular Stackelberg games, to address the issue of effective coordination strategy generation for heterogeneous robots with one-way communication. To that end, focusing on the task of multi-object rearrangement, we develop a theoretical and algorithmic framework that provides strategic guidance for a pair of robot arms, a leader and a follower where the leader has a model of the follower's decision-making process, through the computation of a feedback Stackelberg equilibrium. With built-in tolerance of model uncertainty, the strategic guidance generated by our planning algorithm not only improves the overall efficiency in solving the rearrangement tasks, but is also robust to common pitfalls in collaboration, e.g., chattering.
UR - http://www.scopus.com/inward/record.url?scp=85136326362&partnerID=8YFLogxK
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U2 - 10.1109/ICRA46639.2022.9811678
DO - 10.1109/ICRA46639.2022.9811678
M3 - Conference contribution
AN - SCOPUS:85136326362
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 4922
EP - 4928
BT - 2022 IEEE International Conference on Robotics and Automation, ICRA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Conference on Robotics and Automation, ICRA 2022
Y2 - 23 May 2022 through 27 May 2022
ER -