标题： 解析与改进带有方向校正的最优控制问题 显示英文标题

标题： Interpreting and Improving Optimal Control Problems with Directional Corrections

摘要： 许多机器人任务，例如路径规划或轨迹优化，被表述为最优控制问题（OCPs）。获得高性能的关键在于设计 OCP 的目标函数。实际上，目标函数由一组必须仔细建模并权衡的个体组件组成，以使 OCP 具有期望的解。平衡多个组件以实现期望的解往往具有挑战性，并且当解不理想时，理解各个成本组件的影响也不容易。本文提出了一种基于方向修正概念的框架来解决这些挑战。具体而言，给定一个被认为不理想的 OCP 解以及一位提供改变方向以提高解的可取性的专家，我们的方法分析了各个成本组件与其提供的方向修正的“一致性”。此信息可用于改进 OCP 的公式，例如，通过增加一致成本组件的权重，或减少甚至重新设计不一致成本组件的权重。我们还表明，我们的框架可以自动调整 OCP 的参数以与一组修正保持一致性。 显示英文摘要

摘要： Many robotics tasks, such as path planning or trajectory optimization, are formulated as optimal control problems (OCPs). The key to obtaining high performance lies in the design of the OCP's objective function. In practice, the objective function consists of a set of individual components that must be carefully modeled and traded off such that the OCP has the desired solution. It is often challenging to balance multiple components to achieve the desired solution and to understand, when the solution is undesired, the impact of individual cost components. In this paper, we present a framework addressing these challenges based on the concept of directional corrections. Specifically, given the solution to an OCP that is deemed undesirable, and access to an expert providing the direction of change that would increase the desirability of the solution, our method analyzes the individual cost components for their "consistency" with the provided directional correction. This information can be used to improve the OCP formulation, e.g., by increasing the weight of consistent cost components, or reducing the weight of - or even redesigning - inconsistent cost components. We also show that our framework can automatically tune parameters of the OCP to achieve consistency with a set of corrections.