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Many order fulfillment applications in logistics, such as packing, involvepicking objects from unstructured piles before tightly arranging them in binsor shipping containers. Desirable robotic solutions in this space need to below-cost, robust, easily deployable and simple to control. The current workproposes a complete pipeline for solving packing tasks for cuboid objects,given access only to RGB-D data and a single robot arm with a vacuum-basedend-effector, which is also used as a pushing or dragging finger. The pipelineintegrates perception for detecting the objects and planning so as to properlypick and place objects. The key challenges correspond to sensing noise andfailures in execution, which appear at multiple steps of the process. Toachieve robustness, three uncertainty-reducing manipulation primitives areproposed, which take advantage of the end-effector's and the workspace'scompliance, to successfully and tightly pack multiple cuboid objects. Theoverall solution is demonstrated to be robust to execution and perceptionerrors. The impact of each manipulation primitive is evaluated in extensivereal-world experiments by considering different versions of the pipeline.Furthermore, an open-source simulation framework is provided for modeling suchpacking operations. Ablation studies are performed within this simulationenvironment to evaluate features of the proposed primitives.

Tight Robot Packing in the Real World: A Complete Manipulation Pipeline with Robust Primitives