Temporal Logic Guided Motion Primitives for Complex Manipulation Tasks with User Preferences
IEEE International Conference on Robotics and Automation(2022)
摘要
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 $\mathbf{PI}^{\mathbf{BB}-\mathbf{TL}}$ algorithm, for complex manipulation tasks with user preferences. In particular, weighted truncated linear temporal logic (wTLTL) is incorporated in the $\mathbf{PI}^{\mathbf{BB}-\mathbf{TL}}$ 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 $\mathbf{PI}^{\mathbf{BB}-\mathbf{TL}}$ algorithm is demonstrated via simulation and experiment.
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关键词
temporal logic guided motion primitives,complex manipulation tasks,user preferences,dynamic movement primitives,DMPs,flexible trajectory learning scheme,motion generation,robotic systems,DMP-based methods,go-to-goal tasks,point-to-point motion planning,temporal logic guided optimization,BB,TL algorithm,particular, weighted truncated linear temporal logic,logically organized action,black-box optimization
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