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Robots show us how to teach them: feedback from robots shapes tutoring behavior during action learning
Anna-Lisa Vollmer1, Manuel Mühlig2, Jochen J Steil3
1Centre for Robotics and Neural Systems, School of Computing and Mathematics, University of Plymouth, Portland Square, Plymouth, United Kingdom.
Plos One
|March 21, 2014
Summary
Robot learning by imitation is not a one-way street. A robot learner
Area of Science:
- Robotics
- Human-Robot Interaction
- Machine Learning
Background:
- Robot learning by imitation relies on detecting tutor actions and relevant parts.
- Existing methods assume unidirectional knowledge transfer from tutor to learner.
Purpose of the Study:
- To challenge the assumption of unidirectional knowledge transfer in robot imitation learning.
- To investigate the influence of robot feedback on human tutor demonstrations.
Main Methods:
- Conducted an extensive user study involving an autonomously interacting robot.
- Analyzed the impact of robot-generated feedback on human movement demonstrations.
Main Results:
- Robot learners, through feedback, significantly influence human tutors' movement demonstrations.
- Robot feedback shapes how tutors signal action-relevant information.
Conclusions:
- Robot action learning is a bidirectional process, not solely unidirectional.
- Advocates for a paradigm shift towards interactive robot learning systems.
- Robot learning benefits significantly from interactive feedback loops.
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