基于MPC引导扰动注入的安全感知模仿学习
Safety-Aware Imitation Learning via MPC-Guided Disturbance Injection
AI总结：
提出MPC-SafeGIL方法，通过MPC引导的对抗性扰动注入增强模仿学习安全性，在四足、视觉导航和四旋翼实验中提升安全与任务性能。
摘要：
模仿学习为从专家示范中学习复杂机器人行为提供了一种有前景的方法。然而，学习到的策略可能会产生导致安全违规的错误，这限制了它们在安全关键应用中的部署。我们提出了MPC-SafeGIL，一种设计时方法，通过在专家示范期间注入对抗性扰动来增强模仿学习的安全性。这使专家能够接触到更广泛的安全关键场景，并允许模仿策略学习鲁棒的恢复行为。我们的方法使用基于采样的模型预测控制（MPC）来近似最坏情况扰动，使其可扩展到高维和黑箱动力学系统。与依赖解析模型或交互式专家的先前工作相比，MPC-SafeGIL直接将安全考虑集成到数据收集过程中。我们通过包括四足运动、视觉运动导航和四旋翼飞行器上的真实世界实验在内的广泛模拟验证了我们的方法，展示了在安全性和任务性能方面的改进。请访问我们的网站：https://leqiu2003.github.io/MPCSafeGIL/
英文摘要：
Imitation Learning has provided a promising approach to learning complex robot behaviors from expert demonstrations. However, learned policies can make errors that lead to safety violations, which limits their deployment in safety-critical applications. We propose MPC-SafeGIL, a design-time approach that enhances the safety of imitation learning by injecting adversarial disturbances during expert demonstrations. This exposes the expert to a broader range of safety-critical scenarios and allows the imitation policy to learn robust recovery behaviors. Our method uses sampling-based Model Predictive Control (MPC) to approximate worst-case disturbances, making it scalable to high-dimensional and black-box dynamical systems. In contrast to prior work that relies on analytical models or interactive experts, MPC-SafeGIL integrates safety considerations directly into data collection. We validate our approach through extensive simulations including quadruped locomotion and visuomotor navigation and real-world experiments on a quadrotor, demonstrating improvements in both safety and task performance. See our website here: https://leqiu2003.github.io/MPCSafeGIL/
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。
