Hand-coding locomotion controllers for modular robots is dif?cult due to their polymorphic nature. Instead, we propose to use a simple and distributed reinforcement learning strategy. ATRON modules with identical controllers can be assembled in any con?guration. To optimize the robot?s locomotion speed its modules independently and in parallel adjust their behavior based on a single global reward signal. In simulation, we study the learning strategy?s performance on different robot con?gurations. On the physical platform, we perform learning experiments with ATRON robots learning to move as fast as possible. We conclude that the learning strategy is effective and may be a practical approach to design gaits.
|Status||Udgivet - 2009|