Greg Kahn's deep RL algorithms allows robots to navigation Berkeley's sidewalks! All the robot gets is a camera view, and supervision signal for when a safety driver told it to stop.

Website: sites.google.com/view/sidewalk-…
Arxiv: arxiv.org/abs/2010.04689
(more below)
The idea is simple: a person follows the robot in a "training" phase (could also watch remotely from the camera), and stops the robot when it does something undesirable -- much like a safety driver might stop an autonomous car.
The robot then tries to take those actions that are least likely to lead to disengagement. The result is a learned policy that can navigate hundreds of meters of Berkeley sidewalks entirely from raw images, without any SLAM, localization, etc., entirely using a learned neural net
This work, which completes Greg's PhD thesis, is the culmination of over five years of research on vision-based navigation. Check out Greg's previous work in this area:

BADGR: sites.google.com/view/badgr
CAPs:
GCG: github.com/gkahn13/gcg

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More from @svlevine

13 Oct
Can we view RL as supervised learning, but where we also "optimize" the data? New blog post by Ben, Aviral, and Abhishek: bair.berkeley.edu/blog/2020/10/1…

The idea: modify (reweight, resample, etc.) the data so that supervised regression onto actions produces better policies. More below:
Standard supervised learning is reliable and simple, but of course if we have random or bad data, supervised learning of policies (i.e., imitation) won't produce good results. However, a number of recently proposed algorithms can allow this procedure to work.
What is needed is to iteratively "modify" the data to make it more optimal than the previous iteration. One way to do this is by conditioning the policy on something about the data, such as a goal or even a total reward value.
Read 5 tweets
25 Jun
Interested in trying out offline RL? Justin Fu's blog post on designing a benchmark for offline RL, D4RL, is now up: bair.berkeley.edu/blog/2020/06/2…

D4RL is quickly becoming the most widely used benchmark for offline RL research! Check it out here: github.com/rail-berkeley/…
An important consideration in D4RL is that datasets for offline RL research should *not* just come from near-optimal policies obtained with other RL algorithms, because this is not representative of how we would use offline RL in the real world. D4RL has a few types of datasets..
"Stitching" data provides trajectories that do not actually accomplish the task, but the dataset contains trajectories that accomplish parts of a task. The offline RL method must stitch these together, attaining much higher reward than the best trial in the dataset.
Read 5 tweets

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