title: Robust sensorimotor representation to physical interaction changes in humanoid motion learning.
abstract: This paper proposes a learning from demonstration system based on a motion feature, called phase transfer sequence. The system aims to synthesize the knowledge on humanoid whole body motions learned during teacher-supported interactions, and apply this knowledge during different physical interactions between a robot and its surroundings. The phase transfer sequence represents the temporal order of the changing points in multiple time sequences. It encodes the dynamical aspects of the sequences so as to absorb the gaps in timing and amplitude derived from interaction changes. The phase transfer sequence was evaluated in reinforcement learning of sitting-up and walking motions conducted by a real humanoid robot and compatible simulator. In both tasks, the robotic motions were less dependent on physical interactions when learned by the proposed feature than by conventional similarity measurements. Phase transfer sequence also enhanced the convergence speed of motion learning. Our proposed feature is original primarily because it absorbs the gaps caused by changes of the originally acquired physical interactions, thereby enhancing the learning speed in subsequent interactions.
authors.forename: Toshihiko
authors.initials: T
authors.lastname: Shimizu
authors.forename: Ryo
authors.initials: R
authors.lastname: Saegusa
authors.forename: Shuhei
authors.initials: S
authors.lastname: Ikemoto
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Ishiguro
authors.forename: Giorgio
authors.initials: G
authors.lastname: Metta
mesh_terms: D003198:Computer Simulation; D007839:Functional Laterality; D006801:Humans; D019359:Knowledge; D007858:Learning / Q000502:physiology*; D009038:Motion; D009043:Motor Activity / Q000502:physiology*; D012371:Robotics; D013561:Symbolism; D013997:Time Factors; D014163:Transfer, Psychology; D016138:Walking