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| Record Number | 1280 |
| Reference Type | Conference Proceedings |
| Author(s) | Schaal, S.;Atkeson, C. G.;Vijayakumar, S. |
| Year | 2000 |
| Title | Real-time robot learning with locally weighted statistical learning |
| Journal/Conference/Book Title | International Conference on Robotics and Automation (ICRA2000) |
| Keywords | real-time robot learning, statistical learning, humanoid robotics, finalist for overall best paper award |
Abstract |
Locally weighted learning (LWL) is a class of statistical learning techniques that provides useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of robotic systems. This paper introduces several LWL algorithms that have been tested successfully in real-time learning of complex robot tasks. We discuss two major classes of LWL, memory-based LWL and purely incremental LWL that does not need to remember any data explicitly. In contrast to the traditional beliefs that LWL methods cannot work well in high-dimensional spaces, we provide new algorithms that have been tested in up to 50 dimensional learning problems. The applicability of our LWL algorithms is demonstrated in various robot learning examples, including the learning of devil-sticking, pole-balancing of a humanoid robot arm, and inverse-dynamics learning for a seven degree-of-freedom robot.
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| Notes | clmc |
| URL(s) | http://www-clmc.usc.edu/publications/S/schaal-ICRA2000.pdf
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| Place Published | San Francisco, April 2000 |
| Short Title | Real-time robot learning with locally weighted statistical learning |
| Papers are available as Adobe PDF ".pdf" files. Adobe Reader is available for free for all computer platforms.
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Page last modified on August 10, 2006, at 06:47 PM
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