Rigid Robot

Rigid robot research focuses on developing robust control strategies and efficient path planning algorithms for precise manipulation and locomotion in complex environments. Current efforts concentrate on improving model accuracy and control through methods like neural ordinary differential equations, quadratic programming, and learning-based approaches such as reinforcement learning and system identification, often incorporating haptic feedback and visual servoing. These advancements aim to enhance the safety, dexterity, and adaptability of rigid robots for applications ranging from industrial automation to assistive robotics, addressing challenges like contact-rich manipulation and navigation in dynamic settings.