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Teaching robots by moulding behavior and scaffolding the environment

Published: 02 March 2006 Publication History

Abstract

Programming robots to carry out useful tasks is both a complex and non-trivial exercise. A simple and intuitive method to allow humans to train and shape robot behaviour is clearly a key goal in making this task easier. This paper describes an approach to this problem based on studies of social animals where two teaching strategies are applied to allow a human teacher to train a robot by moulding its actions within a carefully scaffolded environment. Within these enviroments sets of competences can be built by building stateslash action memory maps of the robot's interaction within that environment. These memory maps are then polled using a k-nearest neighbour based algorithm to provide a generalised competence. We take a novel approach in building the memory models by allowing the human teacher to construct them in a hierarchical manner. This mechanism allows a human trainer to build and extend an action-selection mechanism into which new skills can be added to the robot's repertoire of existing competencies. These techniques are implemented on physical Khepera miniature robots and validated on a variety of tasks.

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Published In

cover image ACM Conferences
HRI '06: Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
March 2006
376 pages
ISBN:1595932941
DOI:10.1145/1121241
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 02 March 2006

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Author Tags

  1. imitation
  2. memory-based learning
  3. scaffolding
  4. social robotics
  5. teaching
  6. zone of proximal development

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HRI06
HRI06: International Conference on Human Robot Interaction
March 2 - 3, 2006
Utah, Salt Lake City, USA

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Overall Acceptance Rate 268 of 1,124 submissions, 24%

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ACM/IEEE International Conference on Human-Robot Interaction
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  • (2023)Teaching Assembly Tasks to Robots Using a 3D Simulation EnvironmentProceedings of the 2nd International Conference of the ACM Greek SIGCHI Chapter10.1145/3609987.3610005(1-8)Online publication date: 27-Sep-2023
  • (2023)Verbally Soliciting Human Feedback in Continuous Human-Robot CollaborationProceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3568162.3576980(290-300)Online publication date: 13-Mar-2023
  • (2021)Morphological Development in Robotic Learning: A SurveyIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2021.305254813:4(750-768)Online publication date: Dec-2021
  • (2021)A Framework of Hybrid Force/Motion Skills Learning for RobotsIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2020.296805613:1(162-170)Online publication date: Mar-2021
  • (2021)Learning in Social Interaction: Perspectives from Psychology and Robotics2021 IEEE International Conference on Development and Learning (ICDL)10.1109/ICDL49984.2021.9515648(1-8)Online publication date: 23-Aug-2021
  • (2021)Investigating the Effects of Robot Engagement Communication on Learning from DemonstrationInternational Journal of Social Robotics10.1007/s12369-021-00825-214:3(789-806)Online publication date: 8-Oct-2021
  • (2021)Building Virtual Laboratory with SimulationsComputer Applications in Engineering Education10.1002/cae.2246730:2(483-489)Online publication date: 11-Oct-2021
  • (2019)Human Adaptation to Human–Robot Shared ControlIEEE Transactions on Human-Machine Systems10.1109/THMS.2018.288471949:2(126-136)Online publication date: Apr-2019
  • (2019)Evaluating Architecture Impacts on Deep Imitation Learning Performance for Autonomous Driving2019 IEEE International Conference on Industrial Technology (ICIT)10.1109/ICIT.2019.8755084(865-870)Online publication date: Feb-2019
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