RADIUM：使用梯度加速采样预测并修复端到端机器人故障
RADIUM: Predicting and Repairing End-to-End Robot Failures using Gradient-Accelerated Sampling
AI总结：
本文提出RADIUM框架，利用近似贝叶斯推断与可微仿真、渲染预测端到端机器人故障并自动修复设计和控制策略，在多样性、成本、收敛速度及硬件鲁棒性上显著优于现有方法。
摘要：
在自主系统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。针对真实世界测试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）预测自主系统可能发生故障的方式，以及 b）自动调整系统的设计和控制策略，以预先缓解这些故障。现有的故障预测工具难以在高维环境参数中搜索，无法高效处理视觉在环系统的端到端测试，并且在发现故障后几乎不能提供关于如何缓解故障的指导。我们通过近似贝叶斯推断的视角来处理这一问题，并使用可微仿真和渲染实现高效的故障案例预测与修复。对于无法获得可微仿真器的情况，我们提供了算法的无梯度版本，并从理论和实验两方面评估了基于梯度的方法与无梯度方法之间的权衡。我们将该方法应用于一系列机器人与控制问题，包括优化机器人群体的搜索模式、无人机编队控制和鲁棒网络控制。与基于优化的证伪方法相比，我们的方法能够预测更加多样、更具代表性的故障模式集合；并且我们发现，使用可微仿真所得到的解决方案成本最高可降低10倍，相对于无梯度技术，收敛所需迭代次数最多可减少2倍。在硬件实验中，我们发现使用本文方法修复控制策略可带来5倍的鲁棒性提升。配套代码和视频可在 https://mit-realm.github.io/radium/ 获取。
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 and control policy to preemptively mitigate those failures. Existing tools for failure prediction struggle to search over high-dimensional environmental parameters, cannot efficiently handle end-to-end testing for systems with vision in the loop, and provide little guidance on how to mitigate failures once they are discovered. We approach this problem through the lens of approximate Bayesian inference and use differentiable simulation and rendering for efficient failure case prediction and repair. For cases where a differentiable simulator is not available, we provide a gradient-free version of our algorithm, and we include a theoretical and empirical evaluation of the trade-offs between gradient-based and gradient-free methods. We apply our approach on a range of robotics and control problems, including optimizing search patterns for robot swarms, UAV formation control, and robust network control. Compared to optimization-based falsification methods, our method predicts a more diverse, representative set of failure modes, and we 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. In hardware experiments, we find that repairing control policies using our method leads to a 5x robustness improvement. Accompanying code and video can be found at https://mit-realm.github.io/radium/
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。
