Learning from examples in unstructured, outdoor environments

J. Sun, T. Mehta, D. Wooden, M. Powers, J. Rehg, T. Balch, Magnus Egerstedt

Research output: Contribution to journalArticlepeer-review


In this paper, we present a multi-pronged approach to the "Learning from Example" problem. In particular, we present a framework for integrating learning into a standard, hybrid navigation strategy, composed of both plan-based and reactive controllers. Based on the classification of colors and textures as either good or bad, a global map is populated with estimates of preferability in conjunction with the standard obstacle information. Moreover, individual feedback mappings from learned features to learned control actions are introduced as additional behaviors in the behavioral suite. A number of real-world experiments are discussed that illustrate the viability of the proposed method.

Original languageEnglish (US)
Pages (from-to)1019-1036
Number of pages18
JournalJournal of Field Robotics
Issue number11-12
StatePublished - Nov 2006
Externally publishedYes

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications


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