通过方向修正提升机器人控制问题的优化效果
Interpreting and Improving Optimal Control Problems with Directional Corrections
- 基于方向修正分析成本项与理想解的匹配度
- 可自动调参或重设计不一致的成本项
- 适合需调试复杂优化目标的机器人研究者
许多机器人任务，如路径规划或轨迹优化，可建模为最优控制问题（OCP）。高性能的关键在于目标函数的设计。实际中，目标函数由多个需精心建模和权衡的分量组成，以获得期望解。平衡多个分量常具挑战性，且难以判断解不理想时各成本项的影响。本文提出一种基于方向修正的框架：当某OCP解被判定为不佳时，若专家提供改善方向，该方法可分析各成本项与该方向的一致性。据此可调整权重或重构不一致项。此外，该框架还能自动调节OCP参数以实现与多组修正方向的一致性。
原文摘要 · Abstract (English)
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.
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