FlowDAgger: Human-in-the-Loop Adaptation of Generative Robot Policies in Latent Space
Pretrained generative robot policies based on flow matching and diffusion have achieved impressive results across a wide range of manipulation tasks. Yet real-world deployments routinely expose failure modes outside the pretraining distribution. Closing these gaps typically requires large-scale data collection or online reinforcement learning on physical hardware, which is impractical for rapid and safe adaptation. We present FlowDAgger, a sample- and compute-efficient method for adapting frozen generative robot policies from human interventions in latent space. Our key idea is action inversion: each human expert action is mapped to the noise that would have produced it under the frozen b...
| Authors | Michael Murray, Daphne Chen, Simran Bagaria et al. |
|---|---|
| Primary category | cs.RO |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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