マイクロロボットのための柔軟で再利用可能な歩行プリミティブの学習
Learning Flexible and Reusable Locomotion Primitives for a Microrobot
歩行を文脈付き方策探索としてデータを収集し、多目的歩行プリミティブを学習して計画に利用する手法を提案。動力学モデルなしで250試行以内に学習し迷路走破を実現した。
著者: Brian Yang, Grant Wang, Roberto Calandra, Daniel Contreras, Sergey Levine, Kristofer Pister
分類: cs.RO, cs.AI, cs.LG, stat.ML
原文アブストラクト
The design of gaits for robot locomotion can be a daunting process which requires significant expert knowledge and engineering. This process is even more challenging for robots that do not have an accurate physical model, such as compliant or micro-scale robots. Data-driven gait optimization provides an automated alternative to analytical gait design. In this paper, we propose a novel approach to efficiently learn a wide range of locomotion tasks with walking robots. This approach formalizes locomotion as a contextual policy search task to collect data, and subsequently uses that data to learn multi-objective locomotion primitives that can be used for planning. As a proof-of-concept we consider a simulated hexapod modeled after a recently developed microrobot, and we thoroughly evaluate the performance of this microrobot on different tasks and gaits. Our results validate the proposed controller and learning scheme on single and multi-objective locomotion tasks. Moreover, the experimental simulations show that without any prior knowledge about the robot used (e.g., dynamics model), our approach is capable of learning locomotion primitives within 250 trials and subsequently using them to successfully navigate through a maze.
