MEAM Seminar: “A ‘Full Stack’ Problem”
April 28 at 10:15 AM - 11:15 AM
While many of the tremendous advances seen in robotic dexterity over the past half-decade have been driven by new motor learning methods, I remain convinced that manipulation is a “full stack” problem, benefiting from beautiful synergy between all levels of a robotic system. In this talk, I will present examples from our lab’s attempts to cover the manipulation stack, making advances where we can, while trying not to lose sight of the whole. This includes kinematic optimization for wearable bimanual manipulators that enable easy kinesthetic data collection, contact sensors with different form factors and characteristics designed for different integration and capabilities goals, learning for and with human feedback, and finally the convergence of multiple of these building blocks into complete multifingered dexterous hands with new capabilities.
Matei Ciocarlie
Associate Professor of Mechanical Engineering, Columbia University
Matei Ciocarlie is an Associate Professor in the Mechanical Engineering Department at Columbia University, with affiliated appointments in Computer Science and the Data Science Institute. His 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, Matei was a Research Scientist and then Group Manager at Willow Garage, Inc., and then a Senior Research Scientist at Google, Inc. In these positions, Matei contributed to the development of the open-source Robot Operating System (ROS), and led research projects in areas such as hand design, manipulation under uncertainty, and assistive robotics. Matei was awarded Early Career Awards by the IEEE Robotics and Automation Society, the Office of Naval Research, the National Science Foundation, and the Sloan Foundation, and the tactile robotic hand developed in his lab was named one of 2023’s Best Inventions by Time Magazine.