ICML2003 Workshop:

Machine Learning Technologies
for Autonomous Space Applications

Workshop date: Thursday, August 21, 2003

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Many of the significant advances of the machine learning community have come from tackling challenging, real world problems of practical and economic importance. Of particular interest are space exploration missions where robots must operate autonomously in unknown environments. The robots need to maintain their safety while still exploring the region and accomplishing the overall objectives of the mission. Machine learning methods are ideal to assist the robots in these goals by intelligently filtering observations, learning to identify novel experiences, and knowing when the robot needs to call for human help versus when it can solve the issue itself.

This workshop focused on the following critical questions:

  • How can we design algorithms that can train for a long time under controlled situations, but must work almost perfectly in a remote, autonomous setting?
  • How can ML techniques be tested so as to convince someone outside the field that they are reliable, robust, and effective for real space systems? What are the best analogue problems and situations, here on Earth, for the development and study of applicable ML techniques?
  • Are there specific, possibly novel, metrics and methodologies for evaluation that would be most appropriate for these problems?
  • What ML algorithms drawn from other domains (e.g., tasks with a high cost of failure) are applicable to the problems faced by fielded space missions?
  • Can we provide formal performance guarantees for ML algorithms in the constrained and sometimes hostile environments in which remote space systems will exist?
  • How can we strengthen connections between ML researchers and the people making operational decisions for space missions?

Workshop Organizing Committee

Last changed Mon, Nov 24, 2003 13:06:27 .