Cited by 206Reinforcement Learning in RoboticsRobot Manipulation and LearningNeural Networks and Applications

Acquiring robot skills via reinforcement learning

Vijaykumar Gullapalli, Judy A. Franklin, H. Benbrahim·1994·IEEE Control Systems

Abstract

Skill acquisition is a difficult , yet important problem in robot performance. The authors focus on two skills, namely robotic assembly and balancing and on two classic tasks to develop these skills via learning: the peg-in hole insertion task, and the ball balancing task. A stochastic real-valued (SRV) reinforcement learning algorithm is described and used for learning control and the authors show how it can be used with nonlinear multilayer ANNs. In the peg-in-hole insertion task the SRV network successfully learns to insert to insert a peg into a hole with extremely low clearance, in spite of high sensor noise. In the ball balancing task the SRV network successfully learns to balance the ball with minimal feedback.>

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Crossref · 10.1109/37.257890

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33 of 33 references verified0 citation errors · 0 not checkable

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Authors
Gullapalli, V., Franklin, J. A., & Benbrahim, H.
Affiliations
  • University of Massachusetts Amherst (US)
  • GTE (United States) (US)
Venue
IEEE Control Systems
Published
1994-02
Volume
14
Issue
1
Pages
13-24
ISSN
  • 1066-033X (ISSN-L)
  • 1941-000X
OpenAlex
W1988071341
Cited by
206
References
33
Citations by year
2026: 2 · 2025: 2 · 2024: 6 · 2023: 3 · 2022: 15 · 2021: 11 · 2020: 10 · 2019: 11 · 2018: 3 · 2017: 4 · 2016: 5 · 2015: 5 · 2014: 2 · 2013: 5 · 2012: 5
FWCI
12.36
Type
journal article
Language
en
Open access
closed
Locations
IEEE Control Systems (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
  • Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Keywords
robot skill learning, reinforcement learning, robotic assembly, ball balancing, peg-in-hole insertion, multilayer neural networks, learning control, sensor noise

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