论文标题
具有用户偏好的复杂操作任务的时间逻辑指导运动原始
Temporal Logic Guided Motion Primitives for Complex Manipulation Tasks with User Preferences
论文作者
论文摘要
动态运动原语（DMP）是一种灵活的轨迹学习方案，广泛用于机器人系统的运动产生。但是，现有的基于DMP的方法主要集中于简单的目标任务。这项工作的动机是处理超出点对点运动计划的任务，介绍了运动原始词的时间逻辑指导优化，即PIBB-TL算法，用于具有用户偏好的复杂操作任务。特别是，PIBB-TL算法中合并了加权截短的线性时间逻辑（WTLTL），该算法不仅可以编码复杂的任务，这些任务涉及一系列具有用户偏好的逻辑组织动作计划，而且还提供了方便有效的手段来设计成本功能。然后，对黑盒优化进行调整以识别DMP的最佳形状参数，以实现机器人系统的运动计划。通过模拟和实验证明了PIBB-TL算法的有效性
Dynamic movement primitives (DMPs) are a flexible trajectory learning scheme widely used in motion generation of robotic systems. However, existing DMP-based methods mainly focus on simple go-to-goal tasks. Motivated to handle tasks beyond point-to-point motion planning, this work presents temporal logic guided optimization of motion primitives, namely PIBB-TL algorithm, for complex manipulation tasks with user preferences. In particular, weighted truncated linear temporal logic (wTLTL) is incorporated in the PIBB-TL algorithm, which not only enables the encoding of complex tasks that involve a sequence of logically organized action plans with user preferences, but also provides a convenient and efficient means to design the cost function. The black-box optimization is then adapted to identify optimal shape parameters of DMPs to enable motion planning of robotic systems. The effectiveness of the PIBB-TL algorithm is demonstrated via simulation and experime
