Decentralized Circle Formation Control for Fish-like Robots in the Real-world via Reinforcement Learning
This addresses formation control for cooperative robots in real-world applications, though it is incremental as it builds on existing RL methods for a specific task.
The paper tackles the circle formation control problem for underactuated fish-like robots with unknown dynamics and disturbances by proposing a decentralized reinforcement learning controller that transfers from simulation to reality without manual tuning, achieving scalability and outperforming other RL algorithms in simulations and real-world experiments.
In this paper, the circle formation control problem is addressed for a group of cooperative underactuated fish-like robots involving unknown nonlinear dynamics and disturbances. Based on the reinforcement learning and cognitive consistency theory, we propose a decentralized controller without the knowledge of the dynamics of the fish-like robots. The proposed controller can be transferred from simulation to reality. It is only trained in our established simulation environment, and the trained controller can be deployed to real robots without any manual tuning. Simulation results confirm that the proposed model-free robust formation control method is scalable with respect to the group size of the robots and outperforms other representative RL algorithms. Several experiments in the real world verify the effectiveness of our RL-based approach for circle formation control.