[Submitted on 9 Jul 2026]
Title:FlowDAgger: Human-in-the-Loop Adaptation of Generative Robot Policies in Latent Space
View a PDF of the paper titled FlowDAgger: Human-in-the-Loop Adaptation of Generative Robot Policies in Latent Space, by Michael Murray and 9 other authors
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Abstract: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 base policy, using reverse-time integration followed by local refinement. The resulting inverted noise provides supervision for a lightweight latent policy that steers the base model at deployment time, enabling rapid skill acquisition while preserving its behavioral priors. We evaluate FlowDAgger in simulation and on real-world bimanual and single-arm manipulation, adapting both action-head VLAs and world-action models from a handful of interventions. FlowDAgger outperforms supervised fine-tuning and latent-space RL baselines and preserves pretrained skills on held-out tasks, offering a practical path for adapting robot foundation models in the real world. Website: this https URL
Subjects:
Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:2607.08877 [cs.RO]
(or arXiv:2607.08877v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.08877
arXiv-issued DOI via DataCite
Submission history
From: Michael Murray [view email] [v1] Thu, 9 Jul 2026 19:07:33 UTC (2,975 KB)
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