Tool Manipulation

Tool manipulation research focuses on enabling robots to effectively use tools in unstructured environments, driven by the need for adaptable and robust automation. Current efforts concentrate on integrating large language models (LLMs) with robotic systems for planning and control, leveraging neural networks for adaptive tool-tip control and handling deformable tools, and exploring novel robotic designs like dynamically shifting center-of-mass aerial manipulators for enhanced force generation. These advancements are significant for accelerating robotic learning through techniques like meta-learning and imitation learning from video demonstrations, ultimately aiming to improve the efficiency and adaptability of robots in various real-world tasks.