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This paper introduces a reinforcement learning (RL) framework to model and enhance the navigational capabilities of cyborg cockroaches in bio-inspired swarm robotics. The RL agent learns to control cockroach movement through electrical stimulation of antennae by optimizing state-action spaces and reward functions. Experimental results demonstrate that the RL-enhanced bio-hybrid system achieves high prediction accuracy and control fidelity, enabling complex navigational tasks and collaborative swarm behaviors like obstacle avoidance.
Reinforcement learning can effectively bridge the gap between biological adaptability and robotic precision, enabling cyborg cockroaches to perform complex swarm tasks with enhanced control and accuracy.
Bio-inspired swarm robotics is an emerging field at the intersection of biology, robotics, and artificial intelligence, offering novel capabilities by integrating living organisms with robotic systems. This paper presents a groundbreaking approach to behavior modeling and enhancement for biological hybrid cockroach robots using reinforcement learning (RL). The study focuses on developing a hybrid system that combines the natural adaptability of cockroaches with the precision of robotics. The RL method predicts and optimizes cockroach movements to enhance task performance in bio-inspired swarm robotics. The proposed methodology includes modeling cockroach behaviors through data-driven techniques, designing a control framework using RL, and integrating these systems into a swarm robotics architecture. By defining precise state-action spaces and reward functions, the RL model effectively learned to influence cockroach behavior via electrical stimulation of their antennae, guiding them to perform complex navigational tasks. Experimental results demonstrate the efficacy of the system in both simulated and real-world environments. The RL framework achieves high prediction accuracy and control fidelity, significantly enhancing the operational capabilities of the bio-hybrid system. Swarm-level tests reveal the potential of this approach for collaborative tasks, such as obstacle avoidance and collective decision-making. This research contributes to the advancement of bio-hybrid systems by bridging biological adaptability and robotic precision, with implications for search-and-rescue missions and environmental monitoring. We discuss ethical considerations to address the challenges of using live organisms. Future work will explore scaling the swarm system and integrating advanced sensors and AI algorithms.
