Professor Tedrake's research is focused on finding elegant control solutions for interesting (underactuated, stochastic, and/or difficult to model) dynamical systems that he can build and experiment with. He is particularly interested in finding connections between mechanics (especially non-smooth mechanics) and machine learning/optimization theory which enable robust control design for complex mechanical systems. These days he is primarily focused in merging more of the powerful tools from systems theory with machine learning for robotic manipulation. Please see the description of the Robot Locomotion Group for more information.
A robot's physical form and its motion are innately coupled - in order to change its physical design, one must often change the way it moves, and vice versa. Can computers automatically and simultaneously design robot structure and motion?
Eight years ago, Ted Adelson’s research group at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) unveiled a new sensor technology, called GelSight, that uses physical contact with an object to provide a remarkably detailed 3-D map of its surface. Now, by mounting GelSight sensors on the grippers of robotic arms, two MIT teams have given robots greater sensitivity and dexterity. The researchers presented their work in two papers at the International Conference on Robotics and Automation last week.