Intelligent Sensor Based Manipulation With Robotic Hands

Our hand research has focused on enhancing the dexterity of robotic hands and understanding the nature of dexterous manipulation. The premise of the research is that incorporating task level understanding into a manipulation system simplifies robot planning and increases autonomy. The study of task level strategies for dexterous manipulation has led to development of several novel techniques for controlling the fingertip forces during manipulation and fingertip motion planning. The insights into increased autonomy have led to the development of a novel technique for teleoperating robot hands. The traditional technique of teleoperating a robot hand is to use a Dataglove or exoskeleton master; there is a direct mapping from the human hand to the robot hand. This approach has several limitations which we have addressed by using a simpler control interface with a joystick or keyboard. Enhancing the robot hand's autonomy allows for simpler control strategies and gives it greater functionality than by traditional means. Control of the hand is shared between the user and the robot. We have developed a prototype teleoperation system using a Utah/MIT hand. Our research will ultimately have application in medicine and industry, for enhancement of prosthetic hands and the development of more complex robotic grippers.