We consider the problem of rearranging objects in a cluttered and confined space using a robotic manipulator. The goal is to retrieve a target object from the clutter where the target is occluded by other objects. In situations where overhand grasps are not allowed, the robot needs to remove some objects to make the target accessible. In the course of removing the objects, the robot also needs to determine the locations to place the removed objects. If the robot can access enough empty spaces around or inside the clutter, the placement of the objects is trivially simple. If empty spaces are scarce, placing objects should be done in a principled way as an incorrect placement would deplete the empty spaces quickly.In this work, we propose a method that solves the problems of what and where to relocate objects inside the clutter to retrieve the target. Previously, there have been several efficient methods proposed that deal with each of the what and where to relocate problems separately. We solve the problems together using a graph structure constructed from an object configuration. Also, the method runs fast so scalable in the number of objects. Compared to a state-of-the-art method, our method reduces task and motion planning time up to 74.9% (at least 56.7%) and has a higher success rate under a short time limit for planning, which is 3 minutes.

In this article, we present a task and motion planning method for retrieving a target object from clutter using a robotic manipulator. We consider dense and cluttered environments where some objects must be removed in order to retrieve the target without collisions. To ensure a successful execution, the interplay between task planning (what to remove in what order) and motion planning (how to remove) is crucial. Thus, the task and motion planning approach combining a symbolic task planner and a geometric motion planner becomes one of the major paradigms in manipulation planning. However, motion planning in dense clutter often leads to frequent failures, so repetitive task replanning is inevitable. Although symbolic task planners are general and domain-independent, they do not scale; so we need an efficient task planner specialized for dense clutter for fast completion of tasks. We propose a polynomial-time task planner for object manipulation in clutter that can be combined with any motion planner. We aim to optimize the number of pick-and-place actions which often determines the efficiency of object manipulation tasks. We consider common situations that could occur in clutter: 1) all object locations are known, 2) some hidden objects are revealed while relocating some front objects, and 3) the target is hidden until some objects are removed. Our method is shown to reduce the number of pick-and-place actions compared to baseline methods (e.g., at least 28.0% of reduction in a known static environment with 20 objects). We also deploy the proposed method to two physical robots with vision systems to show that our method can solve real-world problems.

We present an algorithm determining where to relocate objects inside a cluttered and confined space while rearranging objects to retrieve a target object. Although methods that decide what to remove have been proposed, planning for the placement of removed objects inside a workspace has not received much attention. Rather, removed objects are often placed outside the workspace, which incurs additional laborious work (e.g., motion planning and execution of the manipulator and the mobile base, perception of other areas). Some other methods manipulate objects only inside the workspace but without a principle so the rearrangement becomes inefficient.In this work, we consider both monotone (each object is moved only once) and non-monotone arrangement problems which have shown to be $\mathcal{N}\mathcal{P}$-hard. Once the sequence of objects to be relocated is given by any existing algorithm, our method aims to minimize the number of pick-and-place actions to place the objects until the target becomes accessible. From extensive experiments, we show that our method reduces the number of pick-and-place actions and the total execution time (the reduction is up to 23.1% and 28.1% respectively) compared to baseline methods while achieving higher success rates.

This paper presents a task and motion planning (TAMP) framework for a robotic manipulator in order to retrieve a target object from clutter. We consider a configuration of objects in a confined space with a high density so no collision-free path to the target exists. The robot must relocate some objects to retrieve the target without collisions. For fast completion of object rearrangement, the robot aims to optimize the number of pick-and-place actions which often determines the efficiency of a TAMP framework.We propose a task planner incorporating motion planning to generate executable plans which aims to minimize the number of pick-and-place actions. In addition to fully known and static environments, our method can deal with uncertain and dynamic situations incurred by occluded views. Our method is shown to reduce the number of pick-and-place actions compared to baseline methods (e.g., at least 28.0% of reduction in a known static environment with 20 objects).