Mobile manipulation problems involving many objects are challenging to solve due to the high dimensionality and multi-modality of their hybrid configuration spaces. Planners that perform a purely geometric search are prohibitively slow for solving these problems because they are unable to factor the configuration space. Symbolic task planners can efficiently construct plans involving many varia...

In this paper we present an interface between a symbolic planner and a geometric task planner, which is different to a standard trajectory planner in that the former is able to perform geometric reasoning on abstract entities—tasks. We believe that this approach facilitates a more principled interface to symbolic planning, while also leaving more room for the geometric planner to make independe...

We propose a hybrid approach to motion planning for redundant robots, which combines a powerful control framework with a sampling-based planner. We argue, that a suitably chosen task controller already manages a huge amount of trajectory planning work. However, due to its local approach to obstacle avoidance, it may get stuck in local minima. Hence, it is augmented with a globally acting planne...

We present a set of extensions to the knowledge-level PKS (Planning with Knowledge and Sensing) planner, aimed at improving its ability to generate plans in realworld robotics domains. These extensions include a facility for integrating externally-defined reasoning processes in PKS (e.g., invoking a motion planner), an interval-based fluent representation for capturing the effects of noisy sens...

We extend hierarchical task network planning with task insertion (TIHTN) by introducing state constraints, called TIHTNS. We show that just as for TIHTN planning, all solutions of the TIHTNS planning problem can be obtained by acyclic decomposition and task insertion, entailing that its planexistence problem is decidable without any restriction on decomposition methods. We also prove that the e...

We first describe a robot that solves the Rubik’s cube using separate task and motion planning with sensor-enabled closed-loop execution. The task solver uses iterative deepening Astar to plan the sequence of cube moves. Then a special-purpose grasp planner and controller places the manipulator hands on the cube to execute the task plan. The task requires two hands and two-handed re-grasping ma...

Hierarchical Task Network (HTN) planning with Task Insertion (TIHTN planning) is a formalism that hybridizes classical planning with HTN planning by allowing the insertion of operators from outside the method hierarchy. This additional capability has some practical benefits, such as allowing more flexibility for design choices of HTN models: the task hierarchy may be specified only partially, s...