FlowDAgger：在潜在空间中对生成式机器人策略进行人工参与的自适应调整
FlowDAgger: Human-in-the-Loop Adaptation of Generative Robot Policies in Latent Space
- Microsoft Research（微软研究院）
- University of Washington（华盛顿大学）
- ETH Zurich（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究针对预训练生成式机器人策略在实际部署中出现的问题，提出FlowDAgger方法，通过动作反转从人工干预获取监督，在模拟及现实操纵中评估，该方法优于基线且能保留预训练技能，为机器人基础模型自适应调整提供实用路径。
AI中文摘要：
基于流匹配和扩散的预训练生成式机器人策略在广泛的操纵任务中取得了令人瞩目的成果。然而，实际部署中经常会出现预训练分布之外的故障模式。缩小这些差距通常需要大规模数据收集或在物理硬件上进行在线强化学习，这对于快速安全的自适应调整来说并不实际。我们提出了FlowDAgger，这是一种样本和计算效率高的方法，用于根据潜在空间中的人工干预来调整冻结的生成式机器人策略。我们的关键思想是动作反转：通过逆时间积分并随后进行局部细化，将每个人类专家动作映射到在冻结的基础策略下会产生该动作的噪声。由此产生的反转噪声为一个轻量级的潜在策略提供监督，该策略在部署时引导基础模型，从而在保留其行为先验的同时实现快速技能获取。我们在模拟以及现实世界的双臂和单臂操纵中评估了FlowDAgger，从少数干预中调整动作头VLA和世界动作模型。FlowDAgger优于监督微调以及潜在空间强化学习基线，并在保留任务上保留了预训练技能，为在现实世界中调整机器人基础模型提供了一条实用途径。
英文摘要：
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: https://microsoft.github.io/FlowDAgger
