Closed-loop Weighted Empirical Risk Minimization (CW-ERM) addresses a long-standing problem in imitation learning. It is simple to implement and significantly reduces self-driving collisions by a large margin on real-world datasets. Below we are comparing the same scene using ERM (left) and CW-ERM (right) and can see that CW-ERM avoids a front collision. Find out more about this project here  https://wp-research-uk.github.io/cw-erm/ #WovenPlanet | Woven by Toyota | Facebook
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