Learning to See: Research in Training a Robot Vision System
Abstract
Engineered or hard-coded autonomous behaviors tend to be "brittle," working for a narrow range of conditions but failing outside that range. Trainable robots capable of learning and adapting to new environments and conditions have the potential for greater robustness and reusability. Trainable robots would not be restricted to learning from their own experience, but could potentially integrate models or lessons learned by other similar robots operating in different conditions, thus achieving a "learning force multiplier". In this research we began an investigation of issues and methods in robot learning, from definition of the learning objective, training methods, learning algorithms, and integration of models or lessons from multiple training sessions. Our objective in this initial research was not to develop new robot learning technologies, but to explore issues and approaches across all aspects of robot learning. In this stage of the project, we focused on learning to see, specifically learning to discriminate between "Go" a"?NoGo" terrain.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 2008
- Accession Number
- ADA499601
Entities
People
- Gary Witus
- Robert E. Karlsen