用于高效规划的动态切换人类预测模型
Dynamically Switching Human Prediction Models for Efficient Planning
摘要：
随着涉及机器人和人类的环境变得越来越普遍，在规划过程中考虑人的需求也越来越普遍。为了有效地进行规划，机器人必须能够对人类的行为做出反应，有时甚至影响人类的行为。这需要一个人类模型来预测未来的人类行为。一个简单的模型可能假设人类会继续他们以前所做的事情;一个更复杂的模型可能预测人类会采取最佳行动，而不考虑机器人;而一个更复杂的模型可能会捕捉机器人影响人类的能力。这些模型在计算时间和所得到的机器人计划的性能之间做出不同的权衡。只使用一个人类模型要么浪费计算资源，要么无法处理关键情况。在这项工作中，我们让机器人访问一套人类模型，并使其能够在线评估性能计算权衡。通过估计替代模型如何改善人类预测以及如何转化为性能增益，机器人可以在额外计算合理时动态切换人类模型。我们在驾驶模拟器中的实验展示了机器人如何实现与始终使用最佳人类模型相当的性能，但大大减少了计算量。
英文摘要：
As environments involving both robots and humans become increasingly common, so does the need to account for people during planning. To plan effectively, robots must be able to respond to and sometimes influence what humans do. This requires a human model which predicts future human actions. A simple model may assume the human will continue what they did previously; a more complex one might predict that the human will act optimally, disregarding the robot; whereas an even more complex one might capture the robot's ability to influence the human. These models make different trade-offs between computational time and performance of the resulting robot plan. Using only one model of the human either wastes computational resources or is unable to handle critical situations. In this work, we give the robot access to a suite of human models and enable it to assess the performance-computation trade-off online. By estimating how an alternate model could improve human prediction and how that may translate to performance gain, the robot can dynamically switch human models whenever the additional computation is justified. Our experiments in a driving simulator showcase how the robot can achieve performance comparable to always using the best human model, but with greatly reduced computation.
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。
