通过势场引导的流匹配迈向安全的模仿学习
Towards Safe Imitation Learning via Potential Field-Guided Flow Matching
AI总结：
提出势场引导的流匹配策略（PF2MP），通过从成功演示中同时学习任务策略和障碍物势场来调制流匹配向量场，在不影响任务成功率的前提下显著减少碰撞，实现安全的运动生成。
摘要：
深度生成模型，特别是扩散和流匹配模型，近期在通过模仿学习学习复杂策略方面展现了巨大潜力。然而，生成运动的安全性仍然被忽视，特别是在具有固有障碍物的复杂环境中。在这项工作中，我们通过提出势场引导的流匹配策略（PF2MP）来解决这一关键空白，这是一种新颖的方法，能够同时学习任务策略并从同一组成功的演示中提取表示为势场的与障碍物相关的信息。在推理过程中，PF2MP通过学习到的势场调制流匹配向量场，从而实现安全的运动生成。通过利用这些互补的场，我们的方法在导航任务和机器人操作场景等多样化环境中，在不影响任务成功的情况下实现了更高的安全性。我们在仿真和真实世界环境中评估了PF2MP，证明了其在任务空间和关节空间控制中的有效性。实验结果表明，PF2MP增强了安全性，与基线策略相比，碰撞显著减少。这项工作为在非结构化和障碍物密集的环境中实现更安全的运动生成铺平了道路。
英文摘要：
Deep generative models, particularly diffusion and flow matching models, have recently shown remarkable potential in learning complex policies through imitation learning. However, the safety of generated motions remains overlooked, particularly in complex environments with inherent obstacles. In this work, we address this critical gap by proposing Potential Field-Guided Flow Matching Policy (PF2MP), a novel approach that simultaneously learns task policies and extracts obstacle-related information, represented as a potential field, from the same set of successful demonstrations. During inference, PF2MP modulates the flow matching vector field via the learned potential field, enabling safe motion generation. By leveraging these complementary fields, our approach achieves improved safety without compromising task success across diverse environments, such as navigation tasks and robotic manipulation scenarios. We evaluate PF2MP in both simulation and real-world settings, demonstrating its effectiveness in task space and joint space control. Experimental results demonstrate that PF2MP enhances safety, achieving a significant reduction of collisions compared to baseline policies. This work paves the way for safer motion generation in unstructured and obstaclerich environments.
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