Reconfigurable Manipulator

Reconfigurable manipulators are robotic arms designed to adapt their physical structure and configuration to suit diverse tasks and environments, improving versatility and efficiency compared to fixed-geometry robots. Current research emphasizes developing efficient algorithms for real-time motion planning and control, particularly focusing on hybrid approaches combining task-space and configuration-space optimization, and leveraging deep reinforcement learning for adaptable control across varying morphologies. This work is significant for enabling safer human-robot collaboration in industrial settings, optimizing robot design for specific tasks, and expanding the capabilities of robots in complex or constrained environments, such as those found in remanufacturing or agriculture.