Control Paradigm

Control paradigms for robots are evolving to address challenges in complex and unpredictable environments. Current research focuses on developing robust and efficient control strategies, incorporating techniques like neural networks for environment mapping and obstacle avoidance, hierarchical architectures that integrate AI planning with reactive instincts, and optimization-free methods to reduce computational burden. These advancements aim to improve robot safety, autonomy, and performance in diverse applications, ranging from mobile manipulation in cluttered spaces to bipedal locomotion on uneven terrain. The ultimate goal is to create more reliable and adaptable robots capable of interacting effectively with humans and their surroundings.