Online Learning to Approach a Person With No Regret
| Autor: | Yoonseon Oh, Songhwai Oh, Claire J. Tomlin, Hyemin Ahn, Sungjoon Choi |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Control and Optimization Computer science Biomedical Engineering 02 engineering and technology Personalized learning Field (computer science) Human–robot interaction 020901 industrial engineering & automation Personal space Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Robot kinematics business.industry Mechanical Engineering Regret Object (philosophy) Preference Computer Science Applications Human-Computer Interaction Control and Systems Engineering Face (geometry) 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business |
| Zdroj: | IEEE Robotics and Automation Letters. 3:52-59 |
| ISSN: | 2377-3774 |
| DOI: | 10.1109/lra.2017.2729783 |
| Popis: | Each person has a different personal space and behaves differently when another person approaches. Based on this observation, we propose a novel method to learn how to approach a person comfortably based on the person's preference while avoiding uncomfortable encounters. We propose a personal comfort field to learn each person's preference about an approaching object. A personal comfort field is based on existing theories in anthropology and personalized for each user through repeated encounters. We propose an online method to learn a personal comfort field of a user, i.e., personalized learning, based on the concept from the Gaussian process upper confidence bound and show that the proposed method has no regret asymptotically. The effectiveness of the proposed method has been extensively validated in simulation and real-world experiments. Results show that the proposed method can gradually learn the personalized approaching behavior preferred by the user as the number of encounters increases. |
| Databáze: | OpenAIRE |
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