This project aims to reverse-engineer the human brain's ability to control the hand. The project begins by combining a robotic hand previously developed by the PI with a new type of sensitive skin, with a hundred biomimetic tactile sensors.
The main goal of this project is to understand how it is possible to achieve dextrous, approximately optimal control of a hand, performing familiar but challenging tasks in manipulating objects. New, more advanced learning-based control algorithms will be developed and tested on the four empirical testbeds of the project: (1) robotic manipulation by the biomimetic hand; (2) data from recording of human hands performing the same tasks; (3) computer simulations of physical hands; and (4) computer control of cadaver hands via their tendons. The project will use the same algorithms both as models of human motor control and to go beyond the present state of the art in robotic manipulation; this unified approach to biology and engineering is an essential part of the transformative goals of the COPN topic. Dextrous robotic hands have a wide variety of possible applications in industry, space and national security. Improved understanding of how humans can learn to perform better with their hands will also have broader benefits, particularly for the disabled. The team proposes a vigorous plan for education and outreach, capitalizing on the human interest aspects of the demonstrations they will be developing.
