Date: Thursday, May 4th, 2023

Time: 12:00 PM – 2:00 PM EST

Location: Zoom Meeting (https://gatech.zoom.us/j/93205382501?pwd=TEtOb1FTSkNBeVhvVUZuNVBORldMZz09)

Mandy Xie

Ph.D. Student in Computer Science

School of Interactive Computing

Georgia Institute of Technology

Committee:

Dr. Frank Dellaert (Advisor) – School of Interactive Computing, Georgia Institute of Technology

Dr. Harish Ravichandar (Co-advisor) – School of Interactive Computing, Georgia Institute of Technology

Dr. Seth Hutchinson – School of Interactive Computing, Georgia Institute of Technology

Dr. Byron Boots – School of Computer Science & Engineering, University of Washington

Dr. Nathan Ratliff – Seattle Robotics Lab, Nvidia

Summary:

Given that our world is designed by and for humans with impressive dexterity, it is natural to posit that robots with dexterous manipulation skills can effectively operate in a variety of human environments. However, dexterous manipulation in multi-fingered robots has been a long-standing challenge in robotics due to the fact that dexterity requires complex skills, such as coordinating numerous degrees of freedom, balancing contact forces, and breaking and reestablishing contacts with objects. Existing methods are often limited to simulation, or rely on extensive interactions with the environment and incur considerable computational burden. This thesis makes contributions that will help robots efficiently learn and reliably execute dexterous manipulation skills. First, we develop and investigate a new class of behavioral dynamical systems, we call Geometric Fabrics, that can encode complex behaviors in high dimensions by introducing strong structural inductive biases while retaining expressivity. Second, to circumvent the need for painstaking human effort required to manually design these models, we introduce Neural Geometric Fabrics (NGF) that can be used to efficiently learn generalizable manipulation skills. Third, we present a policy architecture and an associated learning framework that can leverage NGFs to encode skills shared across dexterous manipulation tasks and enable efficient skill transfer to novel tasks.