Acquisition of Goal-Oriented Behaviors for Multi-Legged Robots by CPG and Reinforcement Learning

The effectiveness of reinforcement learning for the real robots such as crawling robot and six-legged robot was demonstrated in our early studies. However, the acquired movements of those robots were the repetition of one step move and wait for observation at every time step. In other words, the acquired patterns were not smooth forward movements. In this paper, we realize that the multi-legged robots such as six-legged robot and four-legged robot acquire the smooth movements to the target direction and to reach the target area. In order to realize these tasks, we apply reinforcement learning to the multi-legged robots in which Central Pattern Generators (CPG) are implemented. CPG parameters to generate efficient walking patterns are optimized by using reinforcement learning. Through the experiments using the real multi-legged robots, we confirm the effectiveness of our method using CPG and reinforcement learning.