Spring 2026 GRASP on Robotics: David Held, Carnegie Mellon University, “Precise and Generalizable Robot Manipulation”
February 20 at 10:30 AM - 11:45 AM
This event will be in-person ONLY in Wu and Chen Auditorium.
ABSTRACT
Robots in factories are still largely limited to structured environments with known object models. How can we bring robots into the more diverse, unstructured settings of our daily lives, where objects may be deformable or articulated and vary widely in shape and appearance, while maintaining reliable performance? A popular direction is to train generalist robot policies on large-scale internet data and broad robot datasets. However, today’s generalist policies still lack the precision needed for robust real-world operation. In this talk, I argue that closing this gap requires learning a hierarchy over robot motion: learning both what subgoals to achieve as well as how to move the robot end-effector to achieve them. I will present hierarchical motion policies that combine high-level subgoal prediction with a learned low-level policy, enabling tasks such as articulated object manipulation and precise insertion, while generalizing to unseen objects and novel configurations.
David Held
Carnegie Mellon University
David Held is an Associate Professor at Carnegie Mellon University in the Robotics Institute and is the director of the RPAD lab: Robots Perceiving And Doing. His research focuses on perceptual robot learning, i.e. developing new methods at the intersection of robot perception and planning to teach robots how to manipulate novel, perceptually challenging, and deformable objects. Prior to coming to CMU, David was a post-doctoral researcher at U.C. Berkeley, and he completed his Ph.D. in Computer Science at Stanford University. David also has a B.S. and M.S. in Mechanical Engineering at MIT. David is a recipient of the Google Faculty Research Award in 2017 and the NSF CAREER Award in 2021.