The existing internet-scale image and video datasets cover a wide range of everyday objects and tasks, bringing the potential of learning policies that have broad generalization. Prior works have explored visual pre-training with different self-supervised objectives, but the generalization capabilities of the learned policies remain relatively unknown. In this work, we take the first step towards this challenge, focusing on how pre-trained representations can help the generalization of the learned policies. We first identify the key bottleneck in using a frozen pre-trained visual backbone for policy learning. We then propose SpawnNet, a novel two-stream architecture that learns to fuse pre-trained multi-layer representations into a separate network to learn a robust policy. Through extensive simulated and real experiments, we demonstrate significantly better categorical generalization compared to prior approaches in imitation learning settings.

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