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

[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.