Online Learning to Approach a Person with No Regret

Hyemin Ahn, Yoonseon Oh, Sungjoon Choi, Claire J. Tomlin, Songhwai Oh

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

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 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.

Original languageEnglish
Article number7987073
Pages (from-to)52-59
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume3
Issue number1
DOIs
Publication statusPublished - 2018 Jan
Externally publishedYes

Keywords

  • Human robot interaction
  • motion and path planning
  • personalized learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

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