Toward Safe and Efficient Human-Robot Interaction via Behavior-Driven Danger Signaling
This work addresses safety and efficiency issues in human-robot interaction, particularly for applications like self-driving cars, but it is incremental as it builds on existing predictive modeling and planning methods.
This paper tackles the problem of safe and efficient human-robot interaction by introducing a danger awareness coefficient to model human awareness and willingness to engage in safety, and proposes an online Bayesian learning method and predictive planning scheme to anticipate human actions and generate safe plans. The effectiveness is demonstrated through simulation studies on a self-driving car and pedestrian interaction.
This paper introduces the notion of danger awareness in the context of Human-Robot Interaction (HRI), which decodes whether a human is aware of the existence of the robot, and illuminates whether the human is willing to engage in enforcing the safety. This paper also proposes a method to quantify this notion as a single binary variable, so-called danger awareness coefficient. By analyzing the effect of this coefficient on the human's actions, an online Bayesian learning method is proposed to update the belief about the value of the coefficient. It is shown that based upon the danger awareness coefficient and the proposed learning method, the robot can build a predictive human model to anticipate the human's future actions. In order to create a communication channel between the human and the robot, to enrich the observations and get informative data about the human, and to improve the efficiency of the robot, the robot is equipped with a danger signaling system. A predictive planning scheme, coupled with the predictive human model, is also proposed to provide an efficient and Probabilistically safe plan for the robot. The effectiveness of the proposed scheme is demonstrated through simulation studies on an interaction between a self-driving car and a pedestrian.