Recursive Bayesian Human Intent Recognition in Shared-Control Robotics

Effective human-robot collaboration in shared control requires reasoning about the intentions of the human user. In this work, we present a mathematical formulation for human intent recognition during assistive teleoperation under shared autonomy. Our recursive Bayesian filtering approach models and fuses multiple non-verbal observations to probabilistically reason about the intended goal of the user. In addition to contextual observations, we model and incorporate the human agent's behavior as goal-directed actions with adjustable rationality to inform the underlying intent. We examine human inference on robot motion and furthermore validate our approach with a human subjects study that evaluates autonomy intent inference performance under a variety of goal scenarios and tasks, by novice subjects. Results show that our approach outperforms existing solutions and demonstrates that the probabilistic fusion of multiple observations improves intent inference and performance for shared-control operation.

I. Introduction

A large variety of application areas involve robots operating alongside people. For example, robots can help people in domestic service, search and rescue, surgery and in driving vehicles. An important application area is assistive robotics, wherein the augmentation of human control of the robot with robotics autonomy (shared control) [1]–[5] can alleviate some of the control burden. One fundamental requirement for effective human-robot collaboration in shared control is human intent recognition. In order to meaningfully assist the human collaborator the robot has to correctly reason about the intended goal of the user from a number of potential task-relevant goals-known as the intent inference problem.