Improving the Morphology and Control Policy of Self-reconfiguring Modular Robots in Dynamic Environment (Student Abstract)

Abstract

Ideally, self-reconfiguration modular robots (SMR) can change their morphology and perform actions related to a specific task in any scene. However, most SMRs only adapt to several specific scenes because their morphology and control policies are designed or trained based on these scenes. Once SMRs meet an unknown scene, especially multiply unknown scenes (called dynamic environment), these policies will be useless. Although some of these policies import evolutionary algorithms to enhance the ability of SMR to explore unknown scenes, they are very time-consuming. The reason is that individual fitness depends on the interaction between SMR and these scenes. We propose a two-stage reconfiguration algorithm (TSRA) without any prior knowledge to address the time-consuming problem. In the two stages, the reconfiguration methods use the evolutionary algorithm (GA) to simultaneously generate scene-fitted morphology and actions. The first stage method uses the estimation neural network to evaluate the individual fitness to run faster and can recommend better policies to the second stage. The second stage method obtains this fitness from scenes and updates the neural network to approximate these scenes. Through experiments, TSRA can find better morphology and control policies than the other two canonical algorithms --- GA and GEM-RL.