An application of learning to robotic planning

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.

[1]  Marvin Minsky,et al.  A framework for representing knowledge , 1974 .

[2]  Earl D. Sacerdott.  Planning in a hierarchy of abstraction spaces , 1973, IJCAI 1973.

[3]  Patrick Henry Winston,et al.  Learning structural descriptions from examples , 1970 .

[4]  Richard Fikes,et al.  STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving , 1971, IJCAI.

[5]  Nils J. Nilsson,et al.  Some New Directions in Robot Problem Solving , 1972 .

[6]  Richard Fikes,et al.  Learning and Executing Generalized Robot Plans , 1993, Artif. Intell..

[7]  Bertram C. Bruce,et al.  Some Relations Between Predicate Calculus and Semantic Net Representations of Discourse , 1971, IJCAI.

[8]  I. Goldstein.  Planning Paradigms — Knowledge for Organizing Models into Programs , 1976 .

[9]  C. Cordell Green,et al.  Application of Theorem Proving to Problem Solving , 1969, IJCAI.

[10]  Donald A. Waterman,et al.  Generalization Learning Techniques for Automating the Learning of Heuristics , 1970, Artif. Intell..

[11]  Carl Hewitt,et al.  Description and Theoretical Analysis (Using Schemata) of Planner: A Language for Proving Theorems and Manipulating Models in a Robot , 1972 .

[12]  Rob Kling,et al.  A Paradigm for Reasoning by Analogy , 1971, IJCAI.

[13]  Nils J. Nilsson,et al.  A Hierarchical Robot Planning and Execution System. , 1973 .

[14]  King-Sun Fu,et al.  Learning control systems--Review and outlook , 1970 .

[15]  Richard J. Waldinger,et al.  The QA4 language applied to robot planning , 1972, AFIPS '72 (Fall, part II).

[16]  Aiko Hormann.  How a computer system can learn , 1964, IEEE Spectrum.

[17]  King-Sun Fu,et al.  Learning control systems and intelligent control systems: An intersection of artifical intelligence and automatic control , 1971 .