To advance the state-of-the-art in this domain, researchers at Google, Stanford, and Columbia recently investigated a machine learning system dubbed Form2Fit, which aims to teach a picker robot with a suction arm the concept of assembling objects into kits. "If robots could learn'how things fit together,' then perhaps they could become more adaptable to new manipulation tasks involving objects they have never seen before, like reconnecting severed pipes, or building makeshift shelters by piecing together debris during disaster response scenarios," wrote research intern Kevin Zakka and robotics research scientist Andy Zeng in a blog post. "It helps to increase the efficiency with which we perform tasks, like assembling DIY furniture kits or packing gifts into a box." As Zakka and Zeng explain, Form2Fit learns to recognize how objects correspond (or "fit") to each other mainly through trial and error. One component -- a two-stream matching algorithm -- infers three-dimensional point representations that communicate not only an object's geometry, but its texture and contextual task-level knowledge.