筋骨格ヒューマノイドによるタスク特化型自己身体制御器の獲得：自動運転におけるペダル操作への応用
Task-specific Self-body Controller Acquisition by Musculoskeletal Humanoids: Application to Pedal Control in Autonomous Driving
筋骨格ヒューマノイドの複雑な身体を対象に、制御入力とタスク状態の時系列関係をニューラルネットで学習し、アクセルペダル操作のリアルタイム制御に応用して有効性を検証した。
著者: Kento Kawaharazuka, Kei Tsuzuki, Shogo Makino, Moritaka Onitsuka, Koki Shinjo, Yuki Asano, Kei Okada, Koji Kawasaki, Masayuki Inaba
分類: cs.RO
原文アブストラクト
The musculoskeletal humanoid has many benefits that human beings have, but the modeling of its complex flexible body is difficult. Although we have developed an online acquisition method of the nonlinear relationship between joints and muscles, we could not completely match the actual robot and its self-body image. When realizing a certain task, the direct relationship between the control input and task state needs to be learned. So, we construct a neural network representing the time-series relationship between the control input and task state, and realize the intended task state by applying the network to a real-time control. In this research, we conduct accelerator pedal control experiments as one application, and verify the effectiveness of this study.
