Details

Unlike most machine learning applications, robotics involves physical constraints that make off-the-shelf learning challenging. Difficulties in large-scale data collection and training present a major roadblock to applying today’s data-intensive algorithms. Robot learning has an additional roadblock in evaluation: every physical space is different, making results across labs inconsistent.

Two common assumptions of the robot learning paradigm limit data efficiency. First, an agent typically assumes isolated environments and no prior experience. Second, agents frequently rely on vision alone to learn tasks. Victoria Dean will present work that lifts these two assumptions, improving the data efficiency of robot learning by leveraging multimodality (images and audio) as well as pretraining. She will then introduce a real-world manipulation benchmark for evaluating the generalization of policy learning methods via shared data and hardware.

Finally, Dean will turn to another robot learning application at a larger scale: optimizing heating and cooling systems in our buildings. Buildings account for nearly 40% of carbon emissions in the U.S., yet most building control policies fail to generalize beyond a single building. She will discuss ongoing work to benchmark generalization via the Smart Buildings Challenge, highlighting how machine learning can tackle pressing sustainability challenges.

Speaker

Victoria Dean is a teaching professor in the Department of Computer Science at Princeton University. Her research interests are at the intersection of machine learning and robotics, investigating generalization, benchmarking and democratization. She teaches across introductory computer science, machine learning and ethics and enjoys implementing real-world projects in my courses. Dean is passionate about teaching and mentoring undergraduate students, with an emphasis on improving diversity, equity, and inclusion.

Prior to joining Princeton, Dean was an assistant professor of computer science at Olin College of Engineering. She did her PhD at CMU's Robotics Institute, advised by Abhinav Gupta and supported by the NSF GRFP and Siebel Scholars. She got her B.S. in computer science from MIT and spent time at DeepMind, Waymo, Deep Genomics, Counsyl, Google and Coursera.