论文标题
强大的机器人控制预测：游戏理论方法
Robust Forecasting for Robotic Control: A Game-Theoretic Approach
论文作者
论文摘要
现代机器人需要准确的预测才能在现实世界中做出最佳决策。例如，自动驾驶汽车需要对其他代理商的未来行动进行准确的预测来计划安全轨迹。当前方法在很大程度上依赖历史时间序列来准确预测未来。但是，完全依靠观察到的历史是有问题的，因为它可能被噪声损坏，有离群值或不能完全代表所有可能的结果。为了解决这个问题，我们提出了一个新的框架，用于生成用于机器人控制的鲁棒预测。为了模拟影响未来预测的现实世界因素，我们介绍了对手的概念，而敌人观察到了历史时间序列，以增加机器人的最终控制成本。具体而言，我们将这种交互作用建模为机器人的预报器和这个假设对手之间的零和两人游戏。我们表明，使用基于梯度的优化技术，我们提出的游戏可以解决到本地NASH均衡。此外，我们表明，接受我们方法训练的预报员在分布外现实世界中的变化数据上的效果要好30.14％，而不是基线。
Modern robots require accurate forecasts to make optimal decisions in the real world. For example, self-driving cars need an accurate forecast of other agents' future actions to plan safe trajectories. Current methods rely heavily on historical time series to accurately predict the future. However, relying entirely on the observed history is problematic since it could be corrupted by noise, have outliers, or not completely represent all possible outcomes. To solve this problem, we propose a novel framework for generating robust forecasts for robotic control. In order to model real-world factors affecting future forecasts, we introduce the notion of an adversary, which perturbs observed historical time series to increase a robot's ultimate control cost. Specifically, we model this interaction as a zero-sum two-player game between a robot's forecaster and this hypothetical adversary. We show that our proposed game may be solved to a local Nash equilibrium using gradient-based optimization techniques. Furthermore, we show that a forecaster trained with our method performs 30.14% better on out-of-distribution real-world lane change data than baselines.
