Our paper “Cooperative Control of Mobile Robots with Stackelberg Learning” was accepted to the upcoming IEEE/RSJ International Conference on Intelligent Robots and Systems! This was joint work with Guohui Ding.
The paper proposes Stackelberg Learning in Cooperative Control (SLiCC), a method for cooperative control of multi-agent systems in partially observable settings. SLiCC is based on an asymmetric prosocial–introspective cooperation framework that links state perception with agents’ decision-making strategies. This framework allows for agents to have different observation scopes, with prosocial and introspective behaviors assigned to agents based on the completeness of their state perception.
- IROSCooperative Control of Mobile Robots with Stackelberg LearningIn Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)