3 accepted papers
This letter presents a bio-robotic multi-agent framework that integrates multi-sensor fusion with multi-agent control strategies for rat robots. The sensors equipped with rat robots, including laser-ranging sensor, camera and infrared sensor. We develop two multi-agent control strategies: a leader-f
Learning from Demonstration (LfD) provides an efficient approach to acquiring diverse underwater skills, with task-parameterized learning enhancing the generalization of policies. However, collecting comprehensive underwater demonstrations across various conditions remains a significant challenge. I
This study introduces MarineGym, a high-performance reinforcement learning platform tailored for underwater robotics. It aims to address the limitations of existing underwater simulation environments in terms of reinforcement learning compatibility, training efficiency, and standardized benchmarking