打破事物的贝叶斯方法：通过采样高效预测并修复失效模式
A Bayesian approach to breaking things: efficiently predicting and repairing failure modes via sampling
AI总结：
提出一种基于近似贝叶斯推理和可微仿真的框架，用于高效预测自主系统的失效模式并自动调整设计以缓解失效，实验证明其比传统方法成本更低、收敛更快且预测的失效模式更具多样性。
摘要：
在自主系统能够部署于安全关键应用之前，我们必须能够理解并验证这些系统的安全性。对于真实世界测试的风险或成本过高的情况，我们提出了一种基于仿真的框架，用于 a) 预测自主系统可能失效的方式，以及 b) 自动调整系统设计以先发制人地缓解这些失效。我们通过近似贝叶斯推理的视角来构建此问题，并使用可微仿真进行高效的失效案例预测与修复。我们将该方法应用于一系列机器人和控制问题，包括优化机器人集群的搜索模式以及降低电力传输网络中断的严重程度。与基于优化的证伪技术相比，我们的方法预测了更多样、更具代表性的失效模式集合，同时我们还发现，与无梯度技术相比，我们使用可微仿真产生的解决方案成本降低高达10倍，且收敛所需的迭代次数减少高达2倍。代码和视频可在 https://mit-realm.github.io/breaking-things/ 找到。
英文摘要：
Before autonomous systems can be deployed in safety-critical applications, we must be able to understand and verify the safety of these systems. For cases where the risk or cost of real-world testing is prohibitive, we propose a simulation-based framework for a) predicting ways in which an autonomous system is likely to fail and b) automatically adjusting the system's design to preemptively mitigate those failures. We frame this problem through the lens of approximate Bayesian inference and use differentiable simulation for efficient failure case prediction and repair. We apply our approach on a range of robotics and control problems, including optimizing search patterns for robot swarms and reducing the severity of outages in power transmission networks. Compared to optimization-based falsification techniques, our method predicts a more diverse, representative set of failure modes, and we also find that our use of differentiable simulation yields solutions that have up to 10x lower cost and requires up to 2x fewer iterations to converge relative to gradient-free techniques. Code and videos can be found at https://mit-realm.github.io/breaking-things/
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。
