Events Calendar

PhD Defense - Shao-Hua Sun

PhD Candidate: Shao-Hua Sun

Time: 2/28 (Mon) 3:30pm PST

Venue: Online (zoom link: https://usc.zoom.us/j/2133002395)

Committee members: Prof. Joseph J. Lim (Chair), Prof. Gaurav Sukhatme, Prof. Stefanos Nikolaidis, Prof. Quan Nguyen (AME dept)


Title: Program-Guided Framework for Interpreting and Acquiring Complex Skills with Learning Robots


Abstract: Recent development in artificial intelligence and machine learning has remarkably advanced machines' ability to understand images and videos, comprehend natural languages and speech, and outperform human experts in complex games. However, building intelligent robots that can physically interact with their surroundings as well as learn to operate in unstructured environments, manipulate unknown objects, and acquire novel skills - to free humans from tedious or dangerous manual work- remains challenging. The focus of my research is to develop a robot learning framework that enables robots to acquire long-horizon and complex skills with hierarchical structures, such as furniture assembly and cooking. Specifically, I aim to devise a robot learning framework which is: (1) interpretable: by decoupling interpreting skill specifications (e.g. demonstrations, reward functions) and executing skills, (2) programmatic: by generalizing from simple instances to complex instances without additional learning, (3) hierarchical: by operating on a proper level of abstraction that enables human users to interpret high-level plans of robots allows for composing primitive skills to solve long-horizon tasks, and (4) modular: by being equipped with modules specialized in different functions (e.g. perception, action) which collaborate, allowing for better generalization. This dissertation discusses a series of research projects toward building such an interpretable, programmatic, hierarchical, and modular robot learning framework.

WebCast Link: https://usc.zoom.us/j/2133002395

Audiences: Everyone Is Invited

Contact: Lizsl De Leon