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Incremental learning of primitive skills from demonstration of a task

Published: 06 March 2011 Publication History

Abstract

In this work, we propose methods for automatically generating primitive skills from demonstration of a task. Additionally, we propose methods for improving the existing primitive skills and adding the new primitive skills incrementally and automatically. To validate our proposed methods, we present experimental results of a human-like robot handling three gestures and a task for making coffee.

References

[1]
H. Yang, A. Park, and S. Lee. Gesture spotting and recognition for human-robot interaction. Robotics, IEEE Transactions on, 23(2):256--270, 2007.
[2]
V. Kruger, D. Herzog, S. Baby, A. Ude, and D. Kragic. Learning Actions from Observations. Robotics & Automation Magazine, IEEE, 17(2):30--43, 2010.

Cited By

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  • (2020)Learning, Improving, and Generalizing Motor Skills for the Peg-in-Hole Tasks Based on Imitation Learning and Self-LearningApplied Sciences10.3390/app1008271910:8(2719)Online publication date: 15-Apr-2020
  • (2019)Primitive Action Segmentation and Encoding during Gesture based Demonstration2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)10.1109/CYBER46603.2019.9066543(1552-1557)Online publication date: Jul-2019
  • (2016)Incremental Learning of Skills in a Task-Parameterized Gaussian Mixture ModelJournal of Intelligent and Robotic Systems10.1007/s10846-015-0290-382:1(81-99)Online publication date: 1-Apr-2016
  • Show More Cited By

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Published In

cover image ACM Conferences
HRI '11: Proceedings of the 6th international conference on Human-robot interaction
March 2011
526 pages
ISBN:9781450305617
DOI:10.1145/1957656

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In-Cooperation

  • RA: IEEE Robotics and Automation Society
  • Human Factors & Ergonomics Soc: Human Factors & Ergonomics Soc
  • The Association for the Advancement of Artificial Intelligence (AAAI)
  • IEEE Systems, Man and Cybernetics Society

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 March 2011

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Author Tags

  1. hidden markov model
  2. incremental learning
  3. kl-divergence
  4. primitive skill
  5. threshold model

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HRI'11
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Overall Acceptance Rate 268 of 1,124 submissions, 24%

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HRI '25
ACM/IEEE International Conference on Human-Robot Interaction
March 4 - 6, 2025
Melbourne , VIC , Australia

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Cited By

View all
  • (2020)Learning, Improving, and Generalizing Motor Skills for the Peg-in-Hole Tasks Based on Imitation Learning and Self-LearningApplied Sciences10.3390/app1008271910:8(2719)Online publication date: 15-Apr-2020
  • (2019)Primitive Action Segmentation and Encoding during Gesture based Demonstration2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)10.1109/CYBER46603.2019.9066543(1552-1557)Online publication date: Jul-2019
  • (2016)Incremental Learning of Skills in a Task-Parameterized Gaussian Mixture ModelJournal of Intelligent and Robotic Systems10.1007/s10846-015-0290-382:1(81-99)Online publication date: 1-Apr-2016
  • (2015)Task Parameterization Using Continuous Constraints Extracted From Human DemonstrationsIEEE Transactions on Robotics10.1109/TRO.2015.249500331:6(1458-1471)Online publication date: 1-Dec-2015
  • (2014)Encoding bi-manual coordination patterns from human demonstrationsProceedings of the 2014 ACM/IEEE international conference on Human-robot interaction10.1145/2559636.2559844(264-265)Online publication date: 3-Mar-2014
  • (2011)Behavior programming by kinesthetic demonstration for a chef robot2011 8th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)10.1109/URAI.2011.6145993(875-875)Online publication date: Nov-2011

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