
Long-Range Robotic Navigation via Automated Reinforcement Learning - lainon
https://ai.googleblog.com/2019/02/long-range-robotic-navigation-via.html
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chroem-
I am happy to see that surrogate model optimization is starting to be used to
its potential, but it's sad to have to watch it from the sidelines.

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mturmon
Agree 100%. From the paper:

"Second, [the final training algorithm] adds Simultaneous Localization and
Mapping (SLAM) maps, which robots use at execution time, as a source for
building the roadmaps. Because SLAM maps are noisy, this change closes the
“sim2real gap”, a phenomenon in robotics where simulation-trained agents
significantly underperform when transferred to real-robots."

Very nice work. They are using simulated environments to train the navigation
system (in a very sample-expensive regime already), and on top of that, going
to the trouble of using the noisy maps from SLAM in the simulator, to mimic
real noise characteristics.

Although I have done work in this domain, I had not heard the "sim2real" term
before. It sounds like a generic phenomenon for systems trained on models but
later used in the real world.

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ivalm
This is no doubt exciting, but I am curious how well they deal with unexpected
obstructions (such as cars at intersections behaving in unexpected fashion).
Remember, we have a lot of issues with self-driving, and the kind of problems
(not quite handling environment correctly) are translatable to this use case
as well...

