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Reward specification is a notoriously difficult problem in reinforcementlearning, requiring extensive expert supervision to design robust rewardfunctions. Imitation learning (IL) methods attempt to circumvent these problemsby utilizing expert demonstrations but typically require a large number ofin-domain expert demonstrations. Inspired by advances in the field ofVideo-and-Language Models (VLMs), we present RoboCLIP, an online imitationlearning method that uses a single demonstration (overcoming the large datarequirement) in the form of a video demonstration or a textual description ofthe task to generate rewards without manual reward function design.Additionally, RoboCLIP can also utilize out-of-domain demonstrations, likevideos of humans solving the task for reward generation, circumventing the needto have the same demonstration and deployment domains. RoboCLIP utilizespretrained VLMs without any finetuning for reward generation. Reinforcementlearning agents trained with RoboCLIP rewards demonstrate 2-3 times higherzero-shot performance than competing imitation learning methods on downstreamrobot manipulation tasks, doing so using only one video/text demonstration.

RoboCLIP: One Demonstration is Enough to Learn Robot Policies