Temporal Logic Guided Motion Primitives for Complex Manipulation Tasks with User Preferences
This work addresses complex manipulation tasks with user preferences for robotics, representing an incremental improvement by extending DMPs with temporal logic.
The paper tackles the limitation of dynamic movement primitives (DMPs) in handling complex manipulation tasks beyond simple go-to-goal scenarios by introducing the PIBB-TL algorithm, which incorporates weighted truncated linear temporal logic (wTLTL) to encode user preferences and optimize motion primitives, demonstrating effectiveness in simulations and experiments.
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