Summary of the ICML-2003 Workshop on Machine Learning Technologies for Autonomous Space Applications
Machine Learning Technologies for Autonomous Space Applications
Workshop Summary

Where We Were and Where To Next

The ICML-2003 workshop on machine learning technologies for autonomous space applications, held on Aug 21 2003, brought together researchers from machine learning, space science, and mission development for a day of rich presentations, discussion, and brainstorming on the role of machine learning in autonomous space exploration.

Workshop Goals and What We Expected

When the organizers conceived of this workshop, we expected (and hoped for) a variety of ideas, issues, and a diverse set of attendees. In general, it seems that our hopes were fulfilled, but they day also held some surprises.

The organizing committee's initial thoughts concerned the technical and social issues that need to be solved to make machine learning (ML) a fieldable technology for autonomous space applications. On the technical side, we envisioned the types of autonomy challenges that arise for different mission profiles -- planetary orbiters, deep space probes, rovers, in-atmosphere flying/gliding/balloning explorers, etc. Some of these require very little autonomy for long periods, followed by short periods of quick decision-making, while others operate continuously in a complex and uncertain environment. We recognized that bandwidth and latency are key problems, leading us to identify robust and efficient communication as the "killer application" for ML in spacecraft. As missions get more complex, more instrumented, longer duration, and further from Earth, our relative degree of direct control and return of data will diminish. We also identified verification/validation of ML algorithms as a key area for performance and acceptance. Finally, we were interested in the gaps between the current theory/practice of ML and the kinds of high-reliability requirements that are necessary for space technologies. We hoped for input from other communities where high-reliability is essential.

From the social perspective, we realized that often overcoming skepticism is one of the largest barriers to fielding a new technology, especially in a high cost-of-failure situation like space exploration. We were interested in issues of how to gain acceptance for ML technologies in space missions, including methods for verification/validation, levels of assurance, and, perhaps most importantly, ideas for bridging communities and feedback on the key concerns of mission planners.

Finally, our greatest hope was for an involved and highly participatory workshop with plenty of discussion and good feedback.

Lessons Learned: Consensus and Surprises

Many of our expectations were amply fulfilled on the day of the workshop itself. The workshop had a diverse group of attendees who brought excellent ideas and discussions to the workshop. The brainstorming activity on ML technologies for the Mars rover seemed to generate excitement and produced a number of good insights. Workshop attendees contributed great background information on current technology and its capabilities and weaknesses (much of which is discussed in the contributed papers, also available on this web site).

Far more important than the fulfilled expectations, though, were the insights and revelations that the organizers had not expected. In particular:

Overall, we felt that the workshop was a great success and are grateful to those who contributed their expertise. Thank you all! We look forward to working with you in the future!

Last changed Mon, Nov 24, 2003 13:09:51 .