A Task and Motion Planning Framework Using Iteratively Deepened AND/OR Graph Networks
Hossein Karami, Antony Thomas, Fulvio Mastrogiovanni
TL;DR
This work addresses the challenge of integrated task and motion planning when the number of sub-tasks is not known a priori and for multi-robot coordination. It proposes TMP-IDAN, which uses Iteratively Deepened Augmented AND/OR Graph Networks to grow problem structure on the fly and ground symbolic plans with continuous motions via an in-loop MoveIt/RRT-based planner. The approach provides probabilistic completeness for both the task and motion layers and demonstrates scalability across cluttered tabletop scenarios, the Tower of Hanoi, and a kitchen domain, including single- and multi-robot setups. The results indicate that task planning remains near-linear in network depth while motion planning dominates computation, supporting practical deployment for complex, real-world TAMP tasks with unknown sub-task counts and multiple agents.
Abstract
In this paper, we present an approach for integrated task and motion planning based on an AND/OR graph network, which is used to represent task-level states and actions, and we leverage it to implement different classes of task and motion planning problems (TAMP). Several problems that fall under task and motion planning do not have a predetermined number of sub-tasks to achieve a goal. For example, while retrieving a target object from a cluttered workspace, in principle the number of object re-arrangements required to finally grasp it cannot be known ahead of time. To address this challenge, and in contrast to traditional planners, also those based on AND/OR graphs, we grow the AND/OR graph at run-time by progressively adding sub-graphs until grasping the target object becomes feasible, which yields a network of AND/OR graphs. The approach is extended to enable multi-robot task and motion planning, and (i) it allows us to perform task allocation while coordinating the activity of a given number of robots, and (ii) can handle multi-robot tasks involving an a priori unknown number of sub-tasks. The approach is evaluated and validated both in simulation and with a real dual-arm robot manipulator, that is, Baxter from Rethink Robotics. In particular, for the single-robot task and motion planning, we validated our approach in three different TAMP domains. Furthermore, we also use three different robots for simulation, namely, Baxter, Franka Emika Panda manipulators, and a PR2 robot. Experiments show that our approach can be readily scaled to scenarios with many objects and robots, and is capable of handling different classes of TAMP problems.