Emerging locomotion behavior by maximizing entropy-based information metrics

The Snakebot, proposed by I.Tanev about 20 years ago, is an inspiring illustration of emergent behavior in a robotic application. It demonstrates how the behaviors of a snake-like robotic platform self-organize when certain information metrics are maximized. The integration of self-organization principles, specifically emergence, with morphological computation can facilitate the development of relatively straightforward yet robust controllers for complex structures, enhancing their flexibility and adaptability to environmental changes and dynamic conditions. Our study focuses on achieving self-organization of the behaviors of the robots by optimizing relevant information metrics and guiding evolution using an appropriate learning methodology. In our work we have studied by extensive simulation a WormBot, modeled after the Snakebot, and we employ deep reinforcement learning techniques to enhance the sensory-motor coordination and locomotion behaviors of the WormBot. We developed an appropriate learning system capable of acquiring several motion models for the robot, by optimizing information metrics, and producing an embodied agent that can move consistently (in this case, in a worm-like manner) in accordance with the robot's morphology. Our results show that the WormBot may acquire coherent locomotion patterns without explicit motion function constraints, relying solely on a metric associated with predictive information and the interactions among the loosely coupled components of the agent (in our case the sections constituting the WormBot).