Proactive Communication for Human-Robot Interaction
As robots move from isolated industrial settings into everyday human environments, we need to consider how they should interact with people. Emerging applications range from delivery and support in warehouses, to home and social services. Compared to isolated workspaces, human environments impose more challenges. For example, in social navigation, the robot needs to reach its destination while avoiding people and navigating in a socially compliant way. However, human behaviors are usually complex, stochastic, and hard to predict, which creates difficulties for the robot to plan appropriate actions.To address these challenges, this thesis focuses on the use of simple, proactive haptic communication to facilitate interactions with humans in various scenarios. The core research idea is that, instead of reacting to humans in the environment, the robot should proactively use communication to exchange information and affect human behaviors. This thesis addresses the following topics: (1) the effects of communication on human behavior, (2) mathematical models that predict these effects and human actions, and (3) algorithms to plan for communications that improve efficiency and performance of the human-robot system. We study these topics in the context of three applications. We first present the design and application of a bidirectional communication scheme for a person-following robot. Through a set of experiments, we show that this communication scheme can effectively bring the user into the loop to improve robot performance. Next we discuss the use of implicit and explicit communication in a mobile robot social navigation scenario. A planning framework is developed to allow the robot to proactively communicate with users to improve its transparency and efficiency. Finally, we present methods for communicating continuous directional information for human navigation guidance. We show that more accurate and efficient guidance policies can be generated by incorporating the uncertainties and biases in human's responses.Altogether, these results demonstrate that understanding human behavior and communicating proactively with humans is useful for robots that work in human environments. By taking into account the stochastic and interactive nature of human behavior, these robot can perform actions that are more communicative, efficient, and trustworthy.