Adapting Robot Behavior for Human--Robot Interaction

• Published in: IEEE transactions on robotics Vol. 24; no. 4; pp. 911 - 916
• Main Authors: Mitsunaga, N., Smith, C., Kanda, T., Ishiguro, H., Hagita, N.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.08.2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation; Adjustment; Applied sciences; Artificial intelligence; Behavior adaptation; Comfort; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Context; Control theory. Systems; Discomfort; Electrical engineering, electronics and photonics; Elektroteknik, elektronik och fotonik; Exact sciences and technology; Face; Feedback; Human; Human robot interaction; human > robot interactions; Human-computer interaction; Humanoid; Humanoid robots; Intelligent robots; Laboratories; Learning; Orbital robotics; Pilots; policy gradient reinforcement learning (PGRL); proxemics; Robotics; Robots; Software; Studies; TECHNOLOGY; TEKNIKVETENSKAP; Timing; Behavior adaptation; human > robot interactions; policy gradient reinforcement learning (PGRL); proxemics; Human; human-robot interactions; Gaze; Man machine system; Reinforcement learning; Optimal policy; Feedback regulation; Timed system; Human factor; Robotics; Humanoid robot; User interface; Preference; Comfort; Facies; Man machine relation; Timing
• ISSN: 1552-3098; 1941-0468; 1941-0468
• DOI: 10.1109/TRO.2008.926867

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. However, most previous research works expected the human to consciously give feedback, which might interfere with the aim of interaction. We 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. The mechanism uses gazing at the robot's face and human movement distance as subconscious body signals that indicate a human's comfort and discomfort. A pilot study with a humanoid robot that has ten interaction behaviors has been conducted. The study result of 12 subjects suggests that the proposed mechanism enables autonomous adaptation to individual preferences. Also, detailed discussion and conclusions are presented.