Due to technical difficulties, captions are unavailable but are forthcoming soon.

Abstract:

What is the key to success in dexterous manipulation: the mechanical structure of the hand, the rich tactile and proprioceptive data it can collect, analysis and planning in the brain, or perhaps all of these? Believing these areas are deeply intertwined, we are pursuing them simultaneously, and this talk will present an overview of our recent results. From a mechanism design perspective, I will present our work on optimizing the transmission mechanism for highly underactuated hands, aiming to build compact yet versatile manipulators for NASA’s Astrobee robots. In recent work, we also jointly optimize the hardware of the hand and its control policy using policy search algorithms, an approach we refer to as “Hardware as Policy”. From a sensing perspective, I will present our optics-based tactile finger, providing accurate touch information over a multi-curved three-dimensional surface with no blind spots. Finally, from a modeling perspective, I will talk about our work on analytical models of grasp stability with realistic contact and non-convex energy dissipation constraints.

Bio:

Matei Ciocarlie is an Associate Professor of Mechanical Engineering at Columbia University. His current work focuses on robot motor control, mechanism and sensor design, planning and learning, all aiming to demonstrate complex motor skills such as dexterous manipulation. Matei completed his Ph.D. at Columbia University in New York; before joining the faculty at Columbia, he was a Research Scientist and Group Manager at Willow Garage, Inc., a privately funded Silicon Valley robotics research lab, and then a Senior Research Scientist at Google, Inc. In recognition of his work, Matei has been awarded the Early Career Award by the IEEE Robotics and Automation Society, a Young Investigator Award by the Office of Naval Research, a CAREER Award by the National Science Foundation, and a Sloan Research Fellowship by the Alfred P. Sloan Foundation

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