论文标题
视觉预测控制任务的运动记忆
A memory of motion for visual predictive control tasks
论文作者
论文摘要
本文解决了有效完成视觉预测控制任务的问题。为此，运动的记忆，包含一组构建的沿线轨迹，用于利用预先计算和处理困难的视觉任务。标准回归技术（例如K-Nearest Neighbors和Gaussian流程回归）用于查询内存，并在线提供温暖的启动，并指向控制优化过程。提出的技术允许控制方案达到高性能，同时保持计算时间限制。使用7轴操纵器进行的模拟和实验结果显示了该方法的有效性。
This paper addresses the problem of efficiently achieving visual predictive control tasks. To this end, a memory of motion, containing a set of trajectories built off-line, is used for leveraging precomputation and dealing with difficult visual tasks. Standard regression techniques, such as k-nearest neighbors and Gaussian process regression, are used to query the memory and provide on-line a warm-start and a way point to the control optimization process. The proposed technique allows the control scheme to achieve high performance and, at the same time, keep the computational time limited. Simulation and experimental results, carried out with a 7-axis manipulator, show the effectiveness of the approach.
