Learning more from less data: experiments with lifelong robot learning
Learning more from less data: experiments with lifelong robot learning
- Author(s): S. Thrun and J. O'Sullivan
- DOI: 10.1049/ic:19960152
For access to this article, please select a purchase option:
Buy conference paper PDF
Buy Knowledge Pack
IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.
IEE Colloquium on Self Learning Robots — Recommend this title to your library
Thank you
Your recommendation has been sent to your librarian.
- Author(s): S. Thrun and J. O'Sullivan Source: IEE Colloquium on Self Learning Robots, 1996 page ()
- Conference: IEE Colloquium on Self Learning Robots
Learning more accurate functions from less data is a key issue in robot learning. This paper investigates robot learning in a lifelong learning framework. In lifelong learning, the learner faces an entire collection of learning tasks, not just a single one. Thus, it provides the opportunity for synergy among multiple tasks. To obtain this synergy, the central question in lifelong learning is how can the learner transfer knowledge across multiple tasks. In this paper we describe a selective approach to lifelong learning, the task clustering (TC) algorithm. TC transfers knowledge across multiple tasks by adjusting the distance metric in nearest neighbour generalization. To increase robustness to unrelated tasks, TC arranges all learning tasks hierarchically. When a new learning task arrives, TC relates it to the task hierarchy, in order to transfers knowledge selectively from the most related tasks only. As a result, TC is more robust than its unselective counterpart. Thus far, TC has been successfully applied to perception tasks involving visual and ultrasonic input, using our mobile robot XAVIER. (3 pages)
Inspec keywords: mobile robots; pattern recognition; generalisation (artificial intelligence); learning (artificial intelligence)
Subjects: Adaptive system theory; Artificial intelligence (theory); Pattern recognition; Mobile robots
Related content
content/conferences/10.1049/ic_19960152
pub_keyword,iet_inspecKeyword,pub_concept
6
6