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Found 3 papers, 1 papers with code
Learning to Drive by Observing the Best and Synthesizing the Worst
Our goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle.
MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks
We propose a novel paradigm of training with a decoder-only model for multimodal tasks, which is surprisingly effective in jointly learning of these disparate vision-language tasks.
ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
Our goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle.
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