Task and motion planning using physics-based reasoning

For everyday manipulation tasks, the combination of task and motion planning is required regarding the need of providing the set of possible subtasks which have to be done and how to perform them. Since many alternative plans may exist, the determination of their feasibility and the identification of the best one is a great challenge in robotics. To address this, this paper proposes: a) a version of GraphPlan (one of the best current approaches to task planning) that has been modified to use ontological knowledge and to allow the retrieval of all possible plans; and b) a physics-based reasoning process that determines the feasibility of the resulting plans and an associated cost that allows to select the best one among them. The proposed framework has been implemented and is illustrated through an example.