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ICRA
2008
IEEE

Sparse incremental learning for interactive robot control policy estimation

8 years 8 months ago
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 teloperation 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.
Daniel H. Grollman, Odest Chadwicke Jenkins
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICRA
Authors Daniel H. Grollman, Odest Chadwicke Jenkins
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