论文标题
通过逆最佳控制逐步学习目标功能
Learning Objective Functions Incrementally by Inverse Optimal Control
论文作者
论文摘要
本文提出了一种反向最佳控制方法，该方法使机器人能够从轨迹段集合中逐步了解控制目标函数。通过逐步说出，这意味着扩大轨迹段的收集，因为随着时间的发展提供了其他段。未知的目标函数被参数化为具有未知权重的特征加权总和。每个轨迹段都是最佳轨迹的小片段。提出的方法表明，每个轨迹段（如果有信息）可以对未知权重构成线性约束，因此，可以通过逐步合并所有信息段来学习目标函数。该方法的有效性显示在模拟的2-Link机器人组和6多种操纵四极管系统上，在每个系统中，每个系统都只有小的演示段。
This paper proposes an inverse optimal control method which enables a robot to incrementally learn a control objective function from a collection of trajectory segments. By saying incrementally, it means that the collection of trajectory segments is enlarged because additional segments are provided as time evolves. The unknown objective function is parameterized as a weighted sum of features with unknown weights. Each trajectory segment is a small snippet of optimal trajectory. The proposed method shows that each trajectory segment, if informative, can pose a linear constraint to the unknown weights, thus, the objective function can be learned by incrementally incorporating all informative segments. Effectiveness of the method is shown on a simulated 2-link robot arm and a 6-DoF maneuvering quadrotor system, in each of which only small demonstration segments are available.
