Robotics and Autonomous Systems Center

Learning Hierarchical Tasks from Situated Interactive Instruction

Shiwali Mohan



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.


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