Balanced Information Gathering and Goal-Oriented Actions in Shared Autonomy

Robotic teleoperation can be a complex task due to factors such as high degree-of-freedom manipulators, operator inexperience, and limited operator situational awareness. To reduce teleoperation complexity, researchers have developed the shared autonomy control paradigm that involves joint control of a robot by a human user and an autonomous control system. We introduce the concept of active learning into shared autonomy by developing a method for systems to leverage information gathering: minimizing the system's uncertainty about user goals by moving to information-rich states to observe user input. We create a framework for balancing information gathering actions, which help the system gain information about user goals, with goal-oriented actions, which move the robot towards the goal the system has inferred from the user. We conduct an evaluation within the context of users who are multitasking that compares pure teleoperation with two forms of shared autonomy: our balanced system and a traditional goal-oriented system. Our results show significant improvements for both shared autonomy systems over pure teleoperation in terms of belief convergence about the user's goal and task completion speed and reveal trade-offs across shared autonomy strategies that may inform future investigations in this space.