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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 for robots to learn to interact with novel, perceptually challenging, and deformable objects. David has applied these ideas to robot manipulation and autonomous driving. 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.

You can also download my CV (last updated December 3, 2024).

My research lies at the intersection of robotics, machine learning, and computer vision.

I am interested in developing new methods for robotic perception and control that can allow robots to operate in the complex environments of our daily lives. I have applied the idea of perceptual robot learning to improve a robot's capabilities in two domains: object manipulation and autonomous driving. In the realm of object manipulation, I am developing methods for robots to learn to manipulate novel objects, perceptually challenging objects (e.g. transparent and specular), and deformable objects (e.g. cloth). Regarding autonomous driving, I am developing methods for self-supervised learning and semi-supervised learning (e.g. learning from unlabeled data). Solving these challenges requires rethinking robot perception and control algorithms to handle these types of tasks.