DF-ExpEnse: Diffusion Filtered Exploration for Sample Efficient Finetuning
A natural recipe for intelligent robotic decision-making is initializing from pretrained generative control policies, which have summarized offline experience, and adapting them to self-collected online experience. We present DF-ExpEnse, an exploration technique that improves the quality of online experience collection, thus increasing finetuning sample-efficiency. DF-ExpEnse leverages the multimodal modeling capabilities of the generative control policy to create an expressive and tractably evaluatable candidate set. It then utilizes an ensemble of critics to identify the action that best balances quality with high exploration interest. In fleet settings, DF-ExpEnse further enables cross...
| Authors | Calvin Luo, Chen Sun, Shuran Song |
|---|---|
| Primary category | cs.RO |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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