センサモータフレームワークにおける自己学習と適応
Self-learning and adaptation in a sensorimotor framework
ガウス過程とRRT*を組み合わせ、ロボットが環境との相互作用からセンサモータパターンを自律学習し、長期計画と適応を実現するフレームワークを提案した。
著者: Ali Ghadirzadeh, Judith Bütepage, Danica Kragic, Mårten Björkman
分類: cs.RO
原文アブストラクト
We present a general framework to autonomously achieve a task, where autonomy is acquired by learning sensorimotor patterns of a robot, while it is interacting with its environment. To accomplish the task, using the learned sensorimotor contingencies, our approach predicts a sequence of actions that will lead to the desirable observations. Gaussian processes (GP) with automatic relevance determination is used to learn the sensorimotor mapping. In this way, relevant sensory and motor components can be systematically found in high-dimensional sensory and motor spaces. We propose an incremental GP learning strategy, which discerns between situations, when an update or an adaptation must be implemented. RRT* is exploited to enable long-term planning and generating a sequence of states that lead to a given goal; while a gradient-based search finds the optimum action to steer to a neighbouring state in a single time step. Our experimental results prove the successfulness of the proposed framework to learn a joint space controller with high data dimensions (10$\times$15). It demonstrates short training phase (less than 12 seconds), real-time performance and rapid adaptations capabilities.
