Learning Hierarchical Tasks from Situated Interactive Instruction
Our research aims at building interactive robots and agents that can expand their knowledge by interacting with human users. In this talk, I will give an overview of our ongoing work on learning novel tasks from linguistic, mixed-initiative instructions. The first part of the talk will address the problems of situated language comprehension for cognitive agents in real-world environments. The second part will focus on task learning. I will discuss the knowledge representations we employ to represent hierarchical, goal-oriented tasks and how this knowledge can be learned from interactions using an explanation-based learning framework.
Shiwali Mohan is a Ph.D. candidate in the department of Computer Science and Engineering at the University of Michigan, Ann Arbor. Her research interests include situated language, interactive learning, and cognitive systems.