论文标题
学习模型的先决条件，用于多种模型
Learning Model Preconditions for Planning with Multiple Models
论文作者
论文摘要
当机器人计划时，不同的模型可以提供不同水平的忠诚度。分析模型通常很快进行评估，但仅在有限的条件范围内起作用。同时，物理模拟器可以有效地建模对象之间的复杂相互作用，但通常在计算上更昂贵。学习何时在各种模型之间切换可以大大提高计划速度和任务成功的可靠性。在这项工作中，我们学习模型偏差估计器（MDE），以预测现实世界状态与通过过渡模型输出的状态之间的误差。 MDE可用于定义一个模型前提，该模型前提描述了哪些过渡是准确建模的。然后，我们提出了一个使用学到的模型前提来在各种模型之间切换的计划者，以便在准确的条件下使用模型，并在可能的情况下更快地确定更快的模型。我们对两个现实世界任务进行评估：将杆放入盒子中，将杆放入封闭的抽屉中。
Different models can provide differing levels of fidelity when a robot is planning. Analytical models are often fast to evaluate but only work in limited ranges of conditions. Meanwhile, physics simulators are effective at modeling complex interactions between objects but are typically more computationally expensive. Learning when to switch between the various models can greatly improve the speed of planning and task success reliability. In this work, we learn model deviation estimators (MDEs) to predict the error between real-world states and the states outputted by transition models. MDEs can be used to define a model precondition that describes which transitions are accurately modeled. We then propose a planner that uses the learned model preconditions to switch between various models in order to use models in conditions where they are accurate, prioritizing faster models when possible. We evaluate our method on two real-world tasks: placing a rod into a box and placing a rod into a closed drawer.
