SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning

We describe a policy learning approach to map visual inputs to driving controls conditioned on turning command that leverages side tasks on semantics and object affordances via a learned representation trained for driving. To learn this representation, we train a squeeze network to drive using annotations for the side task as input... (read more)

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