Knowledge-Enabled Robotic Agents for Shelf Replenishment in Cluttered Retail Environments: (Extended Abstract)

Abstract

Autonomous robots in unstructured and dynamically changing retail environments have to master complex perception, knowledge processing, and manipulation tasks. To enable them to act competently, we propose a framework based on three core components:(0) a knowledge-enabled perception system, capable of combining diverse information sources to cope with occlusions and stacked objects with a variety of textures and shapes, (0) knowledge processing methods produce strategies for tidying up supermarket racks, and (0) the necessary manipulation skills in confined spaces to arrange objects in semi-accessible rack shelves. We demonstrate our framework in an simulated environment as well as on a real shopping rack using a PR2 robot. Typical supermarket products are detected and rearranged in the retail rack, tidying up what was found to be misplaced items.