Abstract
Shared autonomy aims at combining robotic and human control in the execution of remote, teleoperated tasks. This cooperative interaction cannot be brought about without the robot first recognizing the current human intention in a fast and reliable way so that a suitable assisting plan can be quickly instantiated and executed. Eye movements have long been known to be highly predictive of the cognitive agenda unfolding during manual tasks and constitute, hence, the earliest and most reliable behavioral cues for intention estimation. In this study, we present an experiment aimed at analyzing human behavior in simple teleoperated pick-and-place tasks in a simulated scenario and at devising a suitable model for early estimation of the current proximal intention. We show that scan paths are, as expected, heavily shaped by the current intention and that two types of Gaussian Hidden Markov Models, one more scene-specific and one more action-specific, achieve a very good prediction performance, while also generalizing to new users and spatial arrangements. We finally discuss how behavioral and model results suggest that eye movements reflect to some extent the invariance and generality of higher-level planning across object configurations, which can be leveraged by cooperative robotic systems.
Highlights
Shared autonomy has recently emerged as an ideal trade-off between full autonomy and complete teleoperation in the execution of remote tasks
Since the user is setting the goals and the ways to achieve them, this collaborative effort relies on the robotic partner to first recognize the current human intention and only afterwards to decide how much to assist with the execution
We focus on gaze-based intention prediction in teleoperating a robotic gripper in a simulated scenario, to investigate human eye-hand coordination under these conditions and to devise an intention estimation model to be later transferred to a real-world shared autonomy scenario
Summary
Shared autonomy has recently emerged as an ideal trade-off between full autonomy and complete teleoperation in the execution of remote tasks. The benefits of this approach rely on assigning to each party the aspects of the task for which they are better suited. The lower kinematic aspects of action execution are usually left to the robot while higher-level cognitive skills, like task planning and handling unexpected events, are typically concurrently exercised by the human, in a blend that can entail different degrees of autonomy for the robotic part (Goodrich et al, 2013; Beer et al, 2014; Schilling et al, 2016). Intention recognition should happen as early and as naturally as possible for the user to be relieved of explicitly directing the robot and for the robot to timely initiate the assisting
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