Automatic generation of biped locomotion controllers using genetic programming

Generating biped locomotion in robotic platforms is hard. It has to deal with the complexity of the tasks which requires the synchronization of several joints, while monitoring stability. Further, it is also expected to deal with the great heterogeneity of existing platforms. The generation of adaptable locomotion further increases the complexity of the task. In this paper, Genetic Programming (GP) is used as an automatic search method for motion primitives of a biped robot, that optimizes a given criterion. It does so by exploring and exploiting the capabilities and particularities of the platform. In order to increase the adaptability of the achieved solutions, feedback pathways were directly included into the evolutionary process through sensory inputs. Simulations on a physic-based Darwin OP have shown that the system is able to generate a faster gait with a given stride time with improved gait temporal characteristics. Further, the system was able to cope with tilted ground within a specific range of slope angles. The system feasibility to generate locomotion more entrained with the environment was shown.