Inferring Occluded Agent Behavior in Dynamic Games with Noise-Corrupted Observations

Robots and autonomous vehicles must rely on sensor observations, e.g., from lidars and cameras, to comprehend their environment and provide safe, efficient services. In multi-agent scenarios, they must additionally account for other agents' intrinsic motivations, which ultimately determine the observed and future behaviors. Dynamic game theory provides a theoretical framework for modeling the behavior of agents with different objectives who interact with each other over time. Previous works employing dynamic game theory often overlook occluded agents, which can lead to risky navigation decisions. To tackle this issue, this paper presents an inverse dynamic game technique which optimizes the game model itself to infer unobserved, occluded agents' behavior that best explains the observations of visible agents. Our framework concurrently predicts agents' future behavior based on the reconstructed game model. Furthermore, we introduce and apply a novel receding horizon planning pipeline in several simulated scenarios. Results demonstrate that our approach offers 1) robust estimation of agents' objectives and 2) precise trajectory predictions for both visible and occluded agents from observations of only visible agents. Experimental findings also indicate that our planning pipeline leads to safer navigation decisions compared to existing baseline methods

Differential game theory for versatile physical human-robot interaction

The last decades have seen a surge of robots working in contact with humans. However, until now these contact robots have made little use of the opportunities offered by physical interaction and lack a systematic methodology to produce versatile behaviours. Here, we develop an interactive robot controller able to understand the control strategy of the human user and react optimally to their movements. We demonstrate that combining an observer with a differential game theory controller can induce a stable interaction between the two partners, precisely identify each other’s control law, and allow them to successfully perform the task with minimum effort. Simulations and experiments with human subjects demonstrate these properties and illustrate how this controller can induce different representative interaction strategies