Underactuated Robotic System

Underactuated robotic systems, robots with fewer actuators than degrees of freedom, are a focus of robotics research aiming to create efficient and adaptable machines. Current research emphasizes developing control strategies, including reinforcement learning and differential dynamic programming, to effectively manage these systems' complex dynamics, often incorporating models that account for factors like friction and skidding. This research is significant because it enables the design of robots that are lighter, simpler, and potentially more robust in unstructured environments, with applications ranging from material handling to autonomous navigation. The development of data-driven approaches and efficient optimization techniques is improving the design and control of these systems.