Thesis Proposal (Hejia Zhang)

Thesis Proposal Committee members: 
Stefanos Nikolaidis
C.C.-Jay Kuo
Jyo Deshmukh
Jesse Thomason
Daniel Seita
 
Title: Understanding, Learning and Planning for Long-horizon Collaborative Manipulation Tasks
 
Abstract: Robots that assist humans in their daily activities have to perform long-horizon manipulation tasks, such as cooking, table setting tasks, effectively and collaboratively. To successfully perform these tasks,  robots have to address the problem of generating both high-level task action sequences and low-level executable motion trajectories, which is known as the Task-and-Motion Planning (TAMP) problem. In this thesis, we first explore how robots can understand and imitate human collaborative manipulation task plans by watching YouTube videos. We then study the problem of robots executing specified high-level task goals in any unstructured environments. We specifically focus on a subclass of the TAMP problem, namely the Geometric Task-and-Motion Planning (GTAMP) problem. We present a framework that allows robots to perform GTAMP tasks collaboratively. Finally, we discuss the proposed work that will potentially allow robots to collaborate with humans to perform long-horizon collaborative manipulation tasks in the real world.    

Location: Hughes Aircraft Electrical Engineering Center (EEB) - 110

Audiences: Everyone Is Invited

Contact: CS Events