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Robot Action Diagnosis and Experience Correction by Falsifying Parameterised Execution Models

  • When faced with an execution failure, an intelligent robot should be able to identify the likely reasons for the failure and adapt its execution policy accordingly. This paper addresses the question of how to utilise knowledge about the execution process, expressed in terms of learned constraints, in order to direct the diagnosis and experience acquisition process. In particular, we present two methods for creating a synergy between failure diagnosis and execution model learning. We first propose a method for diagnosing execution failures of parameterised action execution models, which searches for action parameters that violate a learned precondition model. We then develop a strategy that uses the results of the diagnosis process for generating synthetic data that are more likely to lead to successful execution, thereby increasing the set of available experiences to learn from. The diagnosis and experience correction methods are evaluated for the problem of handle grasping, such that we experimentally demonstrate the effectiveness of the diagnosis algorithm and show that corrected failed experiences can contribute towards improving the execution success of a robot.

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Document Type:Conference Object
Author:Alex MitrevskiORCiD, Paul G. Plöger, Gerhard Lakemeyer
Parent Title (English):2021 IEEE International Conference on Robotics and Automation (ICRA 2021), May 31 - June 4, 2021, Xi'an, China
First Page:11025
Last Page:11031
ArXiv Id:http://arxiv.org/abs/2105.09599
Date of first publication:2021/10/18
Abstract from preprint and with authors permission.
Keyword:Cognitive robot control; Learning from experience; Robot failure diagnosis
Departments, institutes and facilities:Fachbereich Informatik
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2021/03/01