Past talk · AI/ML Seminar Series
In Pursuit of Dexterous and Generalizable Robot Manipulation using Reinforcement Learning, Imitation Learning, and Foundation Models
Assistant Professor of Computer Science, University of Southern California
- Date & time
- Monday, November 18, 2024 · 1:00 PM
- Location
- Donald Bren Hall 4011
Abstract
The robotics community has seen significant progress in applying machine learning for robot manipulation. However, despite this progress, developing a system capable of generalizable robot manipulation remains fundamentally difficult, especially when manipulating in clutter and adjusting deformable objects such as fabrics, rope, and liquids. Some promising techniques for developing general robot manipulation systems include reinforcement learning, imitation learning, and more recently, leveraging foundation models trained on internet-scale data, such as GPT-4. In this talk, I will discuss our recent work on (1) deep reinforcement learning for dexterous manipulation in clutter, (2) foundation models and imitation learning for bimanual manipulation, and (3) our benchmarks and applications of foundation models for deformable object manipulation.
About the speaker
Daniel Seita is an Assistant Professor in the Computer Science department at the University of Southern California and the director of the Sensing, Learning, and Understanding for Robotic Manipulation (SLURM) Lab. His research interests are in computer vision, machine learning, and foundation models for robot manipulation. Daniel was previously a postdoc at Carnegie Mellon University's Robotics Institute and holds a PhD in computer science from the University of California, Berkeley. He received undergraduate degrees in math and computer science from Williams College.