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For robots to seamlessly interact with humans, we first need to make sure that humans and robots understand one another. Diverse algorithms have been developed to enable robots to learn from humans (i.e., transferring information from humans to robots). In parallel, visual, haptic, and auditory communication interfaces have been designed to convey the robot’s internal state to the human (i.e., transferring information from robots to humans). Prior research often separates these two directions of information transfer, and focuses primarily on either learning algorithms or communication interfaces. By contrast, in this survey we take an interdisciplinary approach to identify common themes and emerging trends that close the loop between learning and communication. Specifically, we survey state-of-the-art methods and outcomes for communicating a robot’s learning back to the human teacher during human-robot interaction. This discussion connects human-in-the-loop learning methods and explainable robot learning with multimodal feedback systems and measures of human-robot interaction. We find that—when learning and communication are developed together—the resulting closed-loop system can lead to improved human teaching, increased human trust, and human-robot co-adaptation. The paper includes a perspective on several of the interdisciplinary research themes and open questions that could advance how future robots communicate their learning to everyday operators. Finally, we implement a selection of the reviewed methods in a case study where participants kinesthetically teach a robot arm. This case study documents and tests an integrated approach for learning in ways that can be communicated, conveying this learning across multimodal interfaces, and measuring the resulting changes in human and robot behavior.
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Modeling Haptic Communication in Cooperative Teams
A means to communicate by touch is established when two humans grasp a common rigid object, and such communication is thought to play a role in the superior performance two humans acting together are able to demonstrate over either agent acting alone. But the superior performance demonstrated by dyads, whether in making point-to-point movements or tracking unpredictable targets, is strictly empirical to date. Mechanistic accounts for the performance improvement and explanations relying on haptic communication have been lacking. In this paper we develop a model of haptic communication across a linkage connecting two agents that provides an explicit means for the dyad to achieve a higher loop gain than either agent acting alone and higher than the two agents acting together without haptic feedback. We show that haptic communication closes an additional feedback loop through the linkage and the sensorimotor control systems of both agents. This feedback loop contributes a new factor to the loop gain and thus a definitive mechanism for the dyad to improve performance. Our model predicts higher internal forces with haptic communication, which have previously been observed. Additional testable hypotheses emerge from the model and create a promising future means to transfer human-human dyad behaviors to human-robot teams.
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- Award ID(s):
- 1825931
- PAR ID:
- 10293805
- Date Published:
- Journal Name:
- 2021 IEEE World Haptics Conference
- Page Range / eLocation ID:
- 433 to 438
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/WHC49131.2021.9517210
