  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
