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Human beings subconsciously adapt their behaviors to a communication partner in order to make interactions run smoothly. In human - robot interactions, not only the human but also the robot is expected to adapt to its partner. Thus, to facilitate human - robot interactions, a robot should be able to read subconscious comfort and discomfort signals from humans and adjust its behavior accordingly, just like a human would. The authors propose an adaptation mechanism based on reinforcement learning that reads subconscious body signals from a human partner, and uses this information to adjust interaction distances, gaze meeting, and motion speed and timing in human - robot interactions.
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