Larry Jackal has set up the LAGR (“Learning Applied to Ground Robotics”) project (and competition) which seems to be quite well designed. Features include:
- Many participants (8 going on 12?)
- Standardized hardware. In the DARPA grand challenge contestants entering with motorcycles are at a severe disadvantage to those entering with a Hummer. Similarly, contestants using more powerful sensors can gain huge advantages.
- Monthly contests, with full feedback (but since the hardware is standardized, only code is shipped). One of the premises of the program is that robust systems are desired. Monthly evaluations at different locations can help measure this and provide data.
- Attacks a known hard problem. (cross country driving)
The New York Times has an interesting article about how DARPA has dropped funding for computer science to universities by about a factor of 2 over the last 5 years and become less directed towards basic research. Partially in response, the number of grant submissions to NSF has grown by a factor of 3 (with the NSF budget staying approximately constant in the interim).
This is the sort of policy decision which may make sense for the defense department, but which means a large hit for basic research on information technology development in the US. For example “darpa funded the invention of the internet” is reasonably correct. This policy decision is particularly painful in the context of NSF budget cuts and the end of extensive phone monopoly funded research at Bell labs.
The good news from a learning perspective is that (based on anecdotal evidence) much of the remaining funding is aimed at learning and learning-related fields. Methods of making good automated predictions obviously have a lot of applications that DARPA cares about and the technology often isn’t there yet.