Skill acquisition from human demonstration using a hidden Markov model

A new approach to skill acquisition in assembly is proposed. An assembly skill is represented by a hybrid dynamic system where a discrete event controller models the skill at the task level. The output of the discrete event controller provides the reference commands for the underlying robot controll...

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Bibliographic Details
Main Authors: Hovland, G.E., Sikka, P., McCarragher, B.J.
Format: Conference Proceeding
Language:English
Subjects:
Assembly systems
Fixtures
Hidden Markov models
Humans
Monitoring
Process control
Robot control
Robot sensing systems
Robotic assembly
Uncertainty
Online Access:Request full text
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Description
Summary:A new approach to skill acquisition in assembly is proposed. An assembly skill is represented by a hybrid dynamic system where a discrete event controller models the skill at the task level. The output of the discrete event controller provides the reference commands for the underlying robot controller. This structure is naturally encoded by a hidden Markov model (HMM). The HMM parameters are obtained by training on sensory data from human demonstrations of the skill. Currently, assembly tasks have to be performed by human operators or by robots using expensive fixtures. Our approach transfers the assembly skill from an expert human operator to the robot, thus making it possible for a robot to perform assembly tasks without the use of expensive fixtures.
ISSN:1050-4729
2577-087X
DOI:10.1109/ROBOT.1996.506571