Robust Control Under Uncertainty via Bounded Rationality and Differential Privacy
Robust Control Under Uncertainty via Bounded Rationality and Differential Privacy
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DOI：
10.1109/icra46639.2022.9811557
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发表时间：
2021-09
期刊：
影响因子：
--
通讯作者：
Vincent Pacelli;Anirudha Majumdar
中科院分区：
文献类型：
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作者：
Vincent Pacelli;Anirudha Majumdar
经济实惠的紧凑型高保真传感器的快速发展（例如，相机和激光雷达）允许机器人构建其状态和环境的详细估计。然而，这种丰富的传感器信息的可用性引入了两个挑战：（i）缺乏分析感测模型，这使得难以设计对传感器故障鲁棒的控制器，以及（ii）在真实的时间中处理高维传感器信息的计算开销。本文使用差分隐私理论解决了这些挑战，该理论允许我们（i）设计对状态估计误差具有有限灵敏度的控制器，以及（ii）限制用于控制的状态信息量（即，在有限理性下进行决策）。由此产生的框架近似的分离原则，并允许我们推导出一个上限的错误的状态估计的三个数量方面的成本：使用一个完美的状态估计，状态估计误差的大小，和差分隐私的水平所产生的成本。我们证明了我们的框架数值上不同的机器人问题，包括非线性系统稳定和运动规划的功效。
The rapid development of affordable and compact high-fidelity sensors (e.g., cameras and LIDAR) allows robots to construct detailed estimates of their states and environments. However, the availability of such rich sensor information introduces two challenges: (i) the lack of analytic sensing models, which makes it difficult to design controllers that are robust to sensor failures, and (ii) the computational expense of processing the high-dimensional sensor information in real time. This paper addresses these challenges using the theory of differential privacy, which allows us to (i) design controllers with bounded sensitivity to errors in state estimates, and (ii) bound the amount of state information used for control (i.e., to impose decision-making under bounded rationality). The resulting framework approximates the separation principle and allows us to derive an upper-bound on the cost incurred with a faulty state estimator in terms of three quantities: the cost incurred using a perfect state estimator, the magnitude of state estimation errors, and the level of differential privacy. We demonstrate the efficacy of our framework numerically on different robotics problems, including nonlinear system stabilization and motion planning.