Abstract
Designed to work safely alongside humans, collaborative robots need to be capable partners in human-robot teams. Besides having key capabilities like detecting gestures, recognizing objects, grasping them, and handing them over, these robots need to seamlessly adapt their behavior for efficient human-robot collaboration. In this context we present the fast, supervised Proactive Incremental Learning (PIL) framework for learning associations between human hand gestures and the intended robotic manipulation actions. With the proactive aspect, the robot is competent to predict the human's intent and perform an action without waiting for an instruction. The incremental aspect enables the robot to learn associations on the fly while performing a task. It is a probabilistic, statistically-driven approach. As a proof of concept, we focus on a table assembly task where the robot assists its human partner. We investigate how the accuracy of gesture detection affects the number of interactions required to complete the task. We also conducted a human-robot interaction study with non-roboticist users comparing a proactive with a reactive robot that waits for instructions.
Highlights
Human teams are exceptionally good at conducting collaborative tasks, from apparently trivial tasks like moving furniture to complex tasks like playing a symphony
While much of our work presented in this paper is focussed on robot learning gesture-action associations, we studied how the gaze of the robot was perceived by the participants of our user study
We proposed a fast, supervised Proactive Incremental Learning (PIL) framework to learn the associations between human hand gestures and robot manipulation actions
Summary
Human teams are exceptionally good at conducting collaborative tasks, from apparently trivial tasks like moving furniture to complex tasks like playing a symphony. Humans can communicate task-relevant information by verbal as well as nonverbal channels such as gestures. This is one of the reasons why working in teams is seen to be beneficial. We need collaborative robots with suchlike capabilities for effective human-robot teams. Thanks to advances in the field of robot control and computer vision, it has been possible to develop frameworks for human-robot teams to perform collaborative tasks. The overall task is composed of sub-tasks like detecting a gesture, identifying the targeted object, grasping the object, handing the object to the user, or placing the object within reach of the user
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