[2021-12-24] Mr. Shao-Hua Sun, University of Southern California (USC), "Program-Guided Framework for Interpreting and Acquiring Complex Skills with Learning Robots"

Poster:SHIH-YU(ERINE) PAIPost date:2021-12-23
Title: Program-Guided Framework for Interpreting and Acquiring Complex
Date: 2021-12-24 2:20pm-3:30pm
Location: R103, CSIE
Speaker: Mr. Shao-Hua Sun, University of Southern California (USC)
Hosted by: Prof. Vivian Chen


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. In this talk, I will discuss a series of projects toward building this framework.
Shao-Hua Sun is a Ph.D. candidate and an Annenberg Fellow in the Department of Computer Science at the University of Southern California (USC) in Cognitive Learning for Vision and Robotics Lab (CLVR), advised by Prof. Joseph J. Lim. His research spans deep learning, robot learning, reinforcement learning, meta-learning, program synthesis, and computer vision. Prior to joining USC, Shao-Hua received his B.S. degree from the Department of Electrical Engineering at National Taiwan University (NTU).
Last modification time:2021-12-24 AM 8:33

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