Fast and resilient manipulation planning for target retrieval in clutter
This addresses the challenge of efficient object retrieval in dense clutter for robotics, though it appears incremental as it builds on existing TAMP methods.
The paper tackles the problem of retrieving a target object from cluttered environments using a robotic manipulator, proposing a task and motion planning framework that reduces the number of pick-and-place actions by at least 28.0% compared to baselines in static settings.
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).