Fast High-Quality Tabletop Rearrangement in Bounded Workspace

Abstract

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.

Using Memory-Based Learning to Solve Tasks with State-Action Constraints

“…This can take the form of rearrangement planning [11][12] [13][14] [15], navigation among movable obstacles (NAMO) [16][17] [18], and manipulation among moveable obstacles (MAMO) [19] [20]. Krontiris and Berkis, and Gao et al presented the idea of using dependency graphs, where interactions between objects are represented in a graph structure for these types of problems [11][12] [14]. We leverage this idea of dependency graphs in our own work.…”