Robot Manipulation Planning Under Linear Temporal Logic Specifications

Abstract

Automated planning for manipulation tasks is highly desirable, for it enables robot manipulators to be used by none robotics experts. This thesis presents one approach to solving manipulation planning for tasks expressed in linear temporal logic (ltl). This approach is based on the synergistic framework, which provides probabilistic completeness guarantees. Even though the synergistic framework has shown to work well for planning for ltl tasks in the navigation domain, it lacked an abstraction that can capture the high dimensionality of manipulation. This thesis enables manipulation planning using the synergistic framework by introducing a manipulation abstraction and modifying the interaction between task and motion planning in the framework. The modified framework is shown to be effectively in case studies in both simulation and physical systems. The case studies also show that the synergistic framework plans for manipulation problems more effective using the manipulation abstraction in comparison with a naive abstraction.