Our paper on online personalized learning is accepted to IEEE Robotics and Automation Letters (RA-L)

[2017.07.07]

The following paper is accepted to the IEEE Robotics and Automation Letters (RA-L):

  • Online Learning to Approach a Person with No-Regret by Hyemin Ahn, Yoonseon Oh, Sungjoon Choi, Claire J. Tomlin, and Songhwai Oh
    • Abstract: 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 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 a user as the number of encounters increases.
    • Video