It looks like the mechanism by which Grand Challenge contestants could provide funding for themselves while building the technology for winning the grand challenge race is now here. This Learning Applied to Ground Robot (LAGR) comes from the following observation taken out the PIP:
"...Current systems for autonomous ground robot navigation typically rely on hand-crafted, hand-tuned algorithms for the tasks of obstacle detection and avoidance. While current systems may work well in open terrain or on roads with no traffic, performance falls short in obstacle-rich environments. In LAGR, algorithms will be created that learn how to navigate based on their own experience and by mimicking human teleoperation. It is expected that systems developed in LAGR will provide a performance breakthrough in navigation through complex terrain....Because of the inherent range limitations of both stereo and LADAR, current systems tend to be “near-sighted,” and are unable to make good judgments about the terrain beyond the local neighborhood of the vehicle. This near-sightedness often causes the vehicles to get caught in cul-de-sacs that could have been avoided if the vehicle had access to information about the terrain at greater distances. Furthermore, the pattern recognition algorithms tend to be non-adaptive and tuned for particular classes of obstacles. The result is that most current systems do not learn from their own experience, so that they may repeatedly lead a vehicle into the same obstacle, or unnecessarily avoid a class of “traversable obstacles” such as tall weeds..."
Tall weed, uh, sound like experience is talking.
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