Active Probing and Influencing Human Behaviors Via Autonomous Agents

Autonomous agents (robots) face tremendous challenges while interacting with heterogeneous human agents in close proximity. One of these challenges is that the autonomous agent does not have an accurate model tailored to the specific human that the autonomous agent is interacting with, which could sometimes result in inefficient human-robot interaction and suboptimal system dynamics. Developing an online method to enable the autonomous agent to learn information about the human model is therefore an ongoing research goal. Existing approaches position the robot as a passive learner in the environment to observe the physical states and the associated human response. This passive design, however, only allows the robot to obtain information that the human chooses to exhibit, which sometimes doesn't capture the human's full intention. In this work, we present an online optimization-based probing procedure for the autonomous agent to clarify its belief about the human model in an active manner. By optimizing an information radius, the autonomous agent chooses the action that most challenges its current conviction. This procedure allows the autonomous agent to actively probe the human agents to reveal information that's previously unavailable to the autonomous agent. With this gathered information, the autonomous agent can interactively influence the human agent for some designated objectives. Our main contributions include a coherent theoretical framework that unifies the probing and influence procedures and two case studies in autonomous driving that show how active probing can help to create better participant experience during influence, like higher efficiency or less perturbations.