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Speaker:

David Held

Talk title:

Precise and Generalizable Robot Manipulation

Date: Friday, June 26, 2026

Time: 3-4 p.m.

Location: Online via Zoom

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 vary widely in shape and appearance, while maintaining reliable performance? A popular direction today 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. I will show how this hierarchical approach has enabled us to achieve both generalizable and precise object manipulation.

Bio:

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 joining CMU, David was a postdoctoral researcher at U.C. Berkeley.  He completed his Ph.D. in Computer Science at Stanford University and his B.S. and M.S. degrees in Mechanical Engineering from MIT.  David is a recipient of the Google Faculty Research Award and the NSF CAREER Award.