Robot Learning through Motion and Interaction
Robots today are typically confined to operate in relatively simple, controlled environments. One reason for these limitations is that current methods for robotic perception and control tend to break down when faced with occlusions, viewpoint changes, poor lighting, unmodeled dynamics, and other challenging but common situations that occur when robots are placed in the real world. I argue that, in order to handle these variations, robots need to learn to understand how the world changes over time: how the environment can change as a result of the robot’s own actions or from the actions of other agents in the environment. I will show how we can apply this idea of understanding changes to a number of robotics problems, such as object tracking and safe robot learning. By learning how the environment can change over time, we can enable robots to operate in the complex, cluttered environments of our daily lives.
David Held is an assistant professor at Carnegie Mellon University in the Robotics Institute. His research focuses on robotic perception for autonomous driving and object manipulation. 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 where he developed methods for perception for autonomous vehicles. 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.