Inferring Occluded Geometry Improves Performance when Retrieving an Object from Dense Clutter
This addresses the challenge of retrieving objects in dense clutter for robotics applications in warehouse and household settings, representing an incremental improvement over existing methods.
The paper tackles the problem of object search in cluttered environments by augmenting a manipulation planner with a deep neural network for shape completion and a volumetric memory system, resulting in a significant reduction in the number of actions needed to find hidden objects.
Object search -- the problem of finding a target object in a cluttered scene -- is essential to solve for many robotics applications in warehouse and household environments. However, cluttered environments entail that objects often occlude one another, making it difficult to segment objects and infer their shapes and properties. Instead of relying on the availability of CAD or other explicit models of scene objects, we augment a manipulation planner for cluttered environments with a state-of-the-art deep neural network for shape completion as well as a volumetric memory system, allowing the robot to reason about what may be contained in occluded areas. We test the system in a variety of tabletop manipulation scenes composed of household items, highlighting its applicability to realistic domains. Our results suggest that incorporating both components into a manipulation planning framework significantly reduces the number of actions needed to find a hidden object in dense clutter.