A Task-Motion Planning Framework Using Iteratively Deepened AND/OR Graph Networks
This work addresses the challenge of efficient object retrieval in cluttered environments for robotics applications, representing an incremental improvement over existing AND/OR graph-based planners.
The paper tackles the problem of retrieving a target object from clutter in robotics by developing a Task-Motion Planning framework using Iteratively Deepened AND/OR Graph Networks (TMP-IDAN), which reduces the number of object re-arrangements and enables faster computations compared to traditional planners, as validated in cluttered table-top scenarios with a Baxter robot.
We present an approach for Task-Motion Planning (TMP) using Iterative Deepened AND/OR Graph Networks (TMP-IDAN) that uses an AND/OR graph network based novel abstraction for compactly representing the task-level states and actions. While retrieving a target object from clutter, the number of object re-arrangements required to grasp the target is not known ahead of time. To address this challenge, in contrast to traditional AND/OR graph-based planners, we grow the AND/OR graph online until the target grasp is feasible and thereby obtain a network of AND/OR graphs. The AND/OR graph network allows faster computations than traditional task planners. We validate our approach and evaluate its capabilities using a Baxter robot and a state-of-the-art robotics simulator in several challenging non-trivial cluttered table-top scenarios. The experiments show that our approach is readily scalable to increasing number of objects and different degrees of clutter.