Integrated control of base construction with multiple robots
Summary
This project aims to realize a self-evolving AI system embedded in a versatile modular multi-agent robot system. Deep reinforcement learning has been studied and has yielded results as an AI for generating and controlling robot movements. However, current research achievements primarily focus on single-body robots or the implementation of individual task learning. To apply these advances to reconfigurable modular robots and heterogeneous robot groups, it is necessary to establish methods that enable the Plug and Play (transfer, reuse, reconfiguration) of learned outcomes. Developing hierarchical reinforcement learning is a promising approach. In this research and development task, we will evaluate the AI technologies developed, particularly using assembly tasks.