Machine Learning Technologies for Autonomous Space Applications Workshop Summary

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:

• In spite of the diversity of mission profiles, they largely have the same concerns: science return, spacecraft safety, efficient use of bandwidth, and resource constraints.
• Resource constraints, in fact, were a leading issue throughout the day. Not only the bandwidth/latency constraints that we had foreseen, but also severe limitations on power consumption, processing cycles, movement, and sensory system bandwidth.
• Risk aversion. While bandwidth/latency may be the killer application for ML, risk is the killer barrier. Not only are mission engineers unwilling to risk a hundred-million-dollar-plus spacecraft to an adaptive control mechanism, but mission scientists are even more unwilling to risk photo imagery and sensor data to an on-board decision process. When you're limited to less than 10 megapixels of data transmission per day, you become very concerned about losing even a fraction of that! Given that the primary purpose of virtually all interplanetary and deep-space missions is science return, it is not surprising that the data transmission needs are foremost.

  It was repeatedly emphasized that overcoming this risk aversion comes down to establishing dialog between the ML designers and the mission planners/scientists and that, above all, the ML designers must meet the mission scientists' needs and not vice versa. It is not adequate to present the kinds of results that are traditionally taken as proof in ML -- theoretical bounds on convergence coupled with empirical learning curves on (often synthetic) data. ML will have to prove itself under much more stringent conditions.

• Workshop participants generally agreed that ML algorithms are almost certainly not ready for the prime-time of spacecraft control. It may be possible, now, to learn control strategies offline (e.g., in simulation and in terrestrial analog hardware) and to embed a fixed, final learned policy in the spacecraft. But our reinforcement learning algorithms are not robust and stable enough to be relied upon in an online learning context. Much work remains to be done on theoretical and empirical assurances of learning rate, performance, and safety.
• The general consensus was that the key to successful ML application in this area is a phased approach and a careful choice of applications. ML designers should strive for their algorithms to not be mission enabling (i.e., on the critical path for mission success).
  • Initial applications, at least, should focus on being "value added" -- allow the mission to do more than it could otherwise, with no threat to mission success, science return, or spacecraft safety. Anomaly and error detection and recovery seemed to be good candidates, as are ground-based tasks such as data analysis.
  • Adaptive user interfaces also seem like a useful and relevant, albeit not actively space-borne, opportunity for ML. Controlling craft and interpreting returned data is challenging and could benefit from some of our techniques.
  • Given the degree of risk aversion, the absolute necessity for data filtering, and the difficulty that scientists have in formally expressing their science return preferences ("Would you prefer to get back the picture of the large, red, smooth rock, or the small, jagged, black rock?"), it seems that preference elicitation is actually a key area. ML may be profitably used for eliciting and encoding scientists' preferences and learning an optimal tradeoff between the potentially competing preferences. The encoded version can be embedded in a fixed, on-board data analysis and filtering package.
  • In the medium-term, the extended mission phase (after the primary mission is successfully completed, but before the end of the useful life of the craft) is a plausible place to argue for ML-based autonomy. With the primary work of the mission accomplished, some of the risk aversion is assuaged. There is, however, a great deal of competition for extended missions, so ML will still have to prove itself to a high standard to earn a place. Successful application in non-mission-critical areas will be good background for making this argument.
  • Another medium-term possibility is focusing on low-budget commercial craft (e.g., low-budget Earth sensing satellites) rather than high-budget science and exploration craft. With total budgets below $10 million per craft, these markets both are less risk averse and could benefit proportionally more from improved autonomy. Autonomy can augment or supplant expensive ground-based human operators, who constitute a much larger fraction of such low-budget projects. The cost savings in this case may offset the increased risk incurred in employing ML control technology.
  • Only in the very long term, after a great deal of proving in lower-risk environments, is it reasonable to tackle getting autonomy into the core control of exploratory science spacecraft. This is disappointing, as a number of the mission concepts that are currently under consideration at NASA could greatly benefit from ML-based adaptive autonomous control, if we could deliver it safely. For example, the Europa Ocean Explorer mission will be exploring a totally unknown environment (that may or may not even be oceanic!) with a need for realtime control and effectively no communication with Earth. This is an incredibly demanding environment for ML, but an attractive one.
• Finally, there was discussion about the respective roles of theory, "pure" simulation (virtual environments), "physical" simulation (real hardware in Earth-bound environments), and flight tests. All attendees agreed that each has its role to play and that ensuring that each stage is bug-free and actually relevant to the next stage is challenging. No real answer to that challenge was presented, but all agreed that these stages are necessary to development of ML for space autonomy (not to mention other related fields). In the short term, a number of attendees described and/or offered data and simulation environments that would be of use to researchers in this area.
• Key areas for ML research in this area include, but are not limited to:
  • Learning under resource constraints.
  • Data filtering.
  • Learning control policies (e.g., reinforcement learning) under strong safety constraints.
  • Methods of verification/validation.
  • Methods for establishing the accuracy of a simulation and its relevance to real flight conditions.
  • Preference elicitation and optimization of on-board decisions to reflect the preference model.
  • Data filtering or adaptive lossy compression in response to elicited preferences.
  • Status anomaly/error detection and correction.

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 .