CPGとニューラルネットワークによる二足歩行の階層制御
Hierarchical Control for Bipedal Locomotion using Central Pattern Generators and Neural Networks
最適化したCPGネットワークで関節運動を生成し、ニューラルネットが低次元信号で歩行方向を調整する階層制御を提案し、NICOロボットのシミュレーションで有効性を示した。
著者: Sayantan Auddy, Sven Magg, Stefan Wermter
分類: cs.NE, cs.LG, cs.RO
原文アブストラクト
The complexity of bipedal locomotion may be attributed to the difficulty in synchronizing joint movements while at the same time achieving high-level objectives such as walking in a particular direction. Artificial central pattern generators (CPGs) can produce synchronized joint movements and have been used in the past for bipedal locomotion. However, most existing CPG-based approaches do not address the problem of high-level control explicitly. We propose a novel hierarchical control mechanism for bipedal locomotion where an optimized CPG network is used for joint control and a neural network acts as a high-level controller for modulating the CPG network. By separating motion generation from motion modulation, the high-level controller does not need to control individual joints directly but instead can develop to achieve a higher goal using a low-dimensional control signal. The feasibility of the hierarchical controller is demonstrated through simulation experiments using the Neuro-Inspired Companion (NICO) robot. Experimental results demonstrate the controller's ability to function even without the availability of an exact robot model.
