潜在策略屏障：通过保持分布内学习鲁棒的视觉运动策略
Latent Policy Barrier: Learning Robust Visuomotor Policies by Staying In-Distribution
AI总结：
提出潜在策略屏障（LPB）框架，通过将专家演示的潜在嵌入作为隐式屏障，分离精确模仿与分布外恢复，利用基础扩散策略和动力学模型提升视觉运动策略的鲁棒性和数据效率。
摘要：
通过行为克隆训练的视觉运动策略容易受到协变量偏移的影响，其中与专家轨迹的微小偏差可能累积成失败。缓解此问题的常见策略包括通过人工干预校正或合成数据增强来扩展训练分布。然而，这些方法通常劳动密集、依赖强任务假设或损害模仿质量。我们引入了潜在策略屏障（LPB），一个用于鲁棒视觉运动策略学习的框架。受控制屏障函数启发，LPB将专家演示的潜在嵌入视为隐式屏障，将安全的分布内状态与不安全的分布外（OOD）状态分开。我们的方法将精确专家模仿和OOD恢复的角色解耦为两个独立模块：一个仅基于专家数据的基础扩散策略，以及一个在专家数据和次优策略 rollout 数据上训练的动力学模型。在推理时，动力学模型预测未来潜在状态并优化它们以保持在专家分布内。模拟和真实世界实验表明，LPB提高了策略鲁棒性和数据效率，使得在有限的专家数据下且无需额外人工校正或注释即可实现可靠的操作。
英文摘要：
Visuomotor policies trained via behavior cloning are vulnerable to covariate shift, where small deviations from expert trajectories can compound into failure. Common strategies to mitigate this issue involve expanding the training distribution through human-in-the-loop corrections or synthetic data augmentation. However, these approaches are often labor-intensive, rely on strong task assumptions, or compromise the quality of imitation. We introduce Latent Policy Barrier, a framework for robust visuomotor policy learning. Inspired by Control Barrier Functions, LPB treats the latent embeddings of expert demonstrations as an implicit barrier separating safe, in-distribution states from unsafe, out-of-distribution (OOD) ones. Our approach decouples the role of precise expert imitation and OOD recovery into two separate modules: a base diffusion policy solely on expert data, and a dynamics model trained on both expert and suboptimal policy rollout data. At inference time, the dynamics model predicts future latent states and optimizes them to stay within the expert distribution. Both simulated and real-world experiments show that LPB improves both policy robustness and data efficiency, enabling reliable manipulation from limited expert data and without additional human correction or annotation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
