Extending Interactive Task Learning
Abstract
In this research, we build on our previous ITL research and propose to significantly expand the generality of ITL beyond games, puzzles, and simple robotic tasks. We will focus on increasing the generality of ITL agents along the following dimensions:1. Expand the types of tasks that can be learned to include not only goal-driventasks, but also optimization, procedural, and composite tasks.2. Expand the types of knowledge that can be learned, achieving ???knowledgecompleteness??? so that all types of knowledge the agents can learn aretheoretically learnable.3. Expand the capabilities for interacting with the environment through richer andbroader manipulation actions including those using tools.The research will be developed and evaluated using real-world robotic platforms,including the Fetch mobile manipulator robot.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 10, 2018
- Source ID
- N000141812337
Entities
People
- John E. Laird
Organizations
- Board of Regents of the University of Michigan
- Office of Naval Research
- United States Navy