Presentation information

Oral presentation

General Session » [General Session] 11. Robot / Real World

[4L2] [General Session] 11. Robot / Real World

Fri. Jun 8, 2018 2:00 PM - 3:20 PM Room L (3F Sapphire Hall Asuka)

座長:飯尾 尊優(筑波大学)

3:00 PM - 3:20 PM

[4L2-04] Interaction Modeling Based on Motion Segmentation Using Coupled GP-HSMM

〇Satoru Oshikawa1, Tomoaki Nakamura1, Takayuki Nagai1, Naoto Iwahashi2, Kotaro Funakoshi3, Mikio Nakano4, Masahide Kaneko1 (1. The University of Electro-Communications, 2. Okayama Prefectural University, 3. Kyoto University, 4. Honda Research Institute Japan Co., Ltd.)

Keywords:Coupled Gaussian process hidden semi Markov model, Segmentation, interaction

In the human community, there are various interactions and humans can learn them by observing them or interacting with others. For realizing robots that can coexist with humans, it is important for robots to be able to learn appropriate interactions in the community. In this paper, we propose the novel model coupled Gaussian process hidden semi- Markov model (Coupled GP-HSMM) that enables robots to learn rules of interaction between two persons by observing it in an unsupervised manner. The continuous motions of the persons are segmented into discrete actions based on GP-HSMM, and relationships between the actions are extracted. Moreover, all corresponding actions are not simultaneously conducted by two persons in actual interaction and coupled GP-HSMM models such lags between actions. We conducted experiments using motion data of interaction games and experimental results showed that coupled GP-HSMM can estimate actions, lags between them and their relationships.