Intention-based coordination and interface design for human-robot cooperative search

In this paper, a multi-agent search scheme is presented that supports the recognition of activities and, thus, learning methods for cooperative human-robot interaction. In our approach, stochastic models of human search activity are used to estimate state for HRI. The robot updates a Probabilistic Distribution Function of the target object using the observations and the estimated state of human peers. By this means the robot can choose places to search to compensate the behavior of human peers. This paper also presents an implicit interface design for robot assisted tasks, which allows the robot to infer the intention of the user and to provide assistance autonomously. It reduces the cognitive workload of the user and therefore is useful for elder care applications. The effectiveness and the efficiency of the proposed approaches are demonstrated in the experimental results.