Fast High-Quality Tabletop Rearrangement in Bounded Workspace
This addresses a common daily task for enabling intelligent robotic manipulation, but it is incremental as it builds on existing planning methods.
The paper tackles the problem of efficiently rearranging many objects on a cluttered tabletop using overhand grasps, where objects may need temporary buffer placements, and shows that their methods quickly generate high-quality solutions and are more robust in solving large-scale instances than existing state-of-the-art approaches.
In this paper, we examine the problem of rearranging many objects on a tabletop in a cluttered setting using overhand grasps. Efficient solutions for the problem, which capture a common task that we solve on a daily basis, are essential in enabling truly intelligent robotic manipulation. In a given instance, objects may need to be placed at temporary positions ("buffers") to complete the rearrangement, but allocating these buffer locations can be highly challenging in a cluttered environment. To tackle the challenge, a two-step baseline planner is first developed, which generates a primitive plan based on inherent combinatorial constraints induced by start and goal poses of the objects and then selects buffer locations assisted by the primitive plan. We then employ the "lazy" planner in a tree search framework which is further sped up by adapting a novel preprocessing routine. Simulation experiments show our methods can quickly generate high-quality solutions and are more robust in solving large-scale instances than existing state-of-the-art approaches. source:github.com/arc-l/TRLB