Abstract
Conventional approaches to robotic planning have focused on the resolution theorem prover, using general-purpose search heuristics, with the desired goal expressed in terms of logical calculus. These approaches suffer from several drawbacks; one major problem encountered in these approaches is the speed of planning. In this paper we describe an approach of applying supervised learning to robotic planning. The learning system is an intermediate one between rote learning and generalization learning, and is based on the concept of analogy. Simulation examples of various robot tasks are presented to demonstrate the significant increase in the systems's planning speed and to compare it with some existing systems.
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