Control of Self-organizing Robots using Multi-agent Deep Reinforcement Learning
Takumu Motosawa, Tetsuro Akagawa · Journal of the Robotics Society of Japan · 2026
Self-organizing robots has been proposed that connects multiple small robots and behaves as one large robot. Conventional self-organizing robots perform the robot coupling operation from a predetermined position and direction. However, since precise position control is required, it takes a considerable amount of time to form an aggregate. Therefore, we have developed self-organizing robots with ``omni-directional connector'' that can be connected from all directions. In this paper, we will implement a coupling operation that does not clearly define the starting position of the connection and does not require precise position control by simulation using multi-agent deep reinforcement learning, and conduct experiments on actual machines.