Sparse incremental learning for interactive robot control policy estimation

We are interested in transferring control policies for arbitrary tasks from a human to a robot. Using interactive demonstration via teleoperation as our transfer scenario, we cast learning as statistical regression over sensor-actuator data pairs. Our desire for interactive learning necessitates alg...

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Bibliographic Details
Published in2008 IEEE International Conference on Robotics and Automation pp. 3315 - 3320
Main Authors Grollman, D.H., Jenkins, O.C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2008
Subjects
Educational robots
Function approximation
Gaussian processes
Ground penetrating radar
Human robot interaction
Machine learning algorithms
Robot control
Robot programming
Robot sensing systems
Robotics and automation
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ISBN1424416469
9781424416462
ISSN1050-4729
DOI10.1109/ROBOT.2008.4543716

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Summary:We are interested in transferring control policies for arbitrary tasks from a human to a robot. Using interactive demonstration via teleoperation as our transfer scenario, we cast learning as statistical regression over sensor-actuator data pairs. Our desire for interactive learning necessitates algorithms that are incremental and realtime. We examine locally weighted projection regression, a popular robotic learning algorithm, and sparse online Gaussian processes in this domain on one synthetic and several robot-generated data sets. We evaluate each algorithm in terms of function approximation, learned task performance, and scalability to large data sets.
ISBN:1424416469
9781424416462
ISSN:1050-4729
DOI:10.1109/ROBOT.2008.4543716