论文标题
建立比较机器人任务复杂性的框架
Towards a Framework for Comparing the Complexity of Robotic Tasks
论文作者
论文摘要
我们的动机是将一个机器人任务相对于另一个机器人任务的复杂性进行比较的问题。为此，我们定义了一个简化的概念，该概念正式为以下直觉形式化：任务1将减少到任务2，如果我们可以有效地将解决任务2的任何策略转换为解决任务1的策略。我们进一步定义了给定机器人的两个任务之间的相对复杂性的定量度量。我们证明了我们还原概念（例如反射性，传递性和反对称性）和相对复杂性度量（例如，非负和单调性）的有用特性。此外，我们提出了用于估计相对复杂度度量的实用算法。我们说明了使用（i）示例可以分析降低的示例进行比较机器人任务的框架，以及（ii）强化学习示例，其中所提出的算法可以估计任务之间的相对复杂性。
We are motivated by the problem of comparing the complexity of one robotic task relative to another. To this end, we define a notion of reduction that formalizes the following intuition: Task 1 reduces to Task 2 if we can efficiently transform any policy that solves Task 2 into a policy that solves Task 1. We further define a quantitative measure of the relative complexity between any two tasks for a given robot. We prove useful properties of our notion of reduction (e.g., reflexivity, transitivity, and antisymmetry) and relative complexity measure (e.g., nonnegativity and monotonicity). In addition, we propose practical algorithms for estimating the relative complexity measure. We illustrate our framework for comparing robotic tasks using (i) examples where one can analytically establish reductions, and (ii) reinforcement learning examples where the proposed algorithm can estimate the relative complexity between tasks.
