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OneDemo: Learning policies and generating data from a single demonstration

OneDemo proposes a reinforcement learning framework that edits a single successful demonstration into trajectories suited to new task configurations; the learned editor serves both as a task policy and as a reusable demonstration generator. On 15 Meta-World tasks, it reaches a 92.7% success rate using an average of 53.7k environment interactions; on 9 DexMimicGen tasks, it generates demonstrations with an average success rate of 98.4%, and BC-RNN policies trained on the generated data reach an average success rate of 99.6% at the same data scale.

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