Shared Autonomy of a Robotic Manipulator for Grasping Under Human Intent Uncertainty Using POMDPs

Abstract

In shared autonomy (SA), accurate user intent prediction is crucial for good robot assistance and avoiding user–robot conflicts. Prior works have relied on passive observation of joystick inputs to predict user intent, which works when the goals are clearly separated or when a common policy exists for multiple goals. However, they may not work well when grasping objects to perform daily activities, as there are multiple ways to grasp the same object. We demonstrate the need for active information-gathering in such cases and show how this can be done in a principled manner by formulating SA as a discrete action partially observable Markov decision process (POMDP), reasoning over high-level actions. One of our insights is that apart from having explicit information-gathering actions and goal-oriented actions, it is important to have actions that move toward a distribution of goals and provide no assistance in the POMDP action space. Compared with a method with no active information-gathering, our method performs tasks faster, requires less user input, and decreases opposing actions, especially for more complex objects, getting higher ratings and preference in our user study.