Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration
This addresses the challenge of reducing cognitive overhead for humans in collaborative tasks, though it is incremental as it builds on existing inference and planning methods.
The paper tackled the problem of human-robot collaboration by developing a scheme where the robot predicts human goals using Bayesian inference and re-plans actions in real time, resulting in significant improvements in both objective and perceived performance, with participants strongly preferring the adaptive robot.
The study of human-robot interaction is fundamental to the design and use of robotics in real-world applications. Robots will need to predict and adapt to the actions of human collaborators in order to achieve good performance and improve safety and end-user adoption. This paper evaluates a human-robot collaboration scheme that combines the task allocation and motion levels of reasoning: the robotic agent uses Bayesian inference to predict the next goal of its human partner from his or her ongoing motion, and re-plans its own actions in real time. This anticipative adaptation is desirable in many practical scenarios, where humans are unable or unwilling to take on the cognitive overhead required to explicitly communicate their intent to the robot. A behavioral experiment indicates that the combination of goal inference and dynamic task planning significantly improves both objective and perceived performance of the human-robot team. Participants were highly sensitive to the differences between robot behaviors, preferring to work with a robot that adapted to their actions over one that did not.