Robot training through incremental learning

Robert E. Karlsen; Shawn Hunt; Gary Witus

doi:10.1117/12.884092

23 May 2011 Robot training through incremental learning

Robert E. Karlsen, Shawn Hunt, Gary Witus

Proceedings Volume 8045, Unmanned Systems Technology XIII; 804504 (2011) https://doi.org/10.1117/12.884092
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

The real world is too complex and variable to directly program an autonomous ground robot's control system to respond to the inputs from its environmental sensors such as LIDAR and video. The need for learning incrementally, discarding prior data, is important because of the vast amount of data that can be generated by these sensors. This is crucial because the system needs to generate and update its internal models in real-time. There should be little difference between the training and execution phases; the system should be continually learning, or engaged in "life-long learning". This paper explores research into incremental learning systems such as nearest neighbor, Bayesian classifiers, and fuzzy c-means clustering.

Citation Download Citation

Robert E. Karlsen, Shawn Hunt, and Gary Witus "Robot training through incremental learning", Proc. SPIE 8045, Unmanned Systems Technology XIII, 804504 (23 May 2011); https://doi.org/10.1117/12.884092

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