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Carnegie Mellon Robotics Institute

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Abstract

Recently, libraries of trajectory plans have been shown to be a promising way of creating policies for difficult problems. However, often it is not desirable or even possible to create a new library for every task. We present a method for transferring libraries across tasks, which allows us to build libraries by learning from demonstration on one task and apply them to similar tasks. Representing the libraries in a feature-based space is key to supporting transfer. We also search through the library to ensure a complete path to the goal is possible. Results are shown for the Little Dog task. Little Dog is a quadruped robot that has to walk across rough terrain at reasonably fast speeds.

Notes

Associated Center(s) / Consortia: Center for the Foundations of Robotics

Associated Lab(s) / Group(s): Planning and Autonomy Lab

Associated Project(s): Learning Locomotion

Text Reference

Martin Stolle, Hanns Tappeiner, Joel Chestnutt, and Chris Atkeson, "Transfer of policies based on trajectory libraries," Proceedings of the International Conference on Intelligent Robots and Systems, 2007.

BibTeX Reference

@inproceedings{Stolle_2007_6097,
   author = "Martin Stolle and Hanns Tappeiner and Joel Chestnutt and Chris Atkeson",
   title = "Transfer of policies based on trajectory libraries",
   booktitle = "Proceedings of the International Conference on Intelligent Robots and Systems",
   year = "2007",
}

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