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There is a large variety of objects and appliances in human environments,such as stoves, coffee dispensers, juice extractors, and so on. It ischallenging for a roboticist to program a robot for each of these object typesand for each of their instantiations. In this work, we present a novel approachto manipulation planning based on the idea that many household objects sharesimilarly-operated object parts. We formulate the manipulation planning as astructured prediction problem and design a deep learning model that can handlelarge noise in the manipulation demonstrations and learns features from threedifferent modalities: point-clouds, language and trajectory. In order tocollect a large number of manipulation demonstrations for different objects, wedeveloped a new crowd-sourcing platform called Robobarista. We test our modelon our dataset consisting of 116 objects with 249 parts along with 250 languageinstructions, for which there are 1225 crowd-sourced manipulationdemonstrations. We further show that our robot can even manipulate objects ithas never seen before.

Robobarista: Object Part based Transfer of Manipulation Trajectories from Crowd-sourcing in 3D Pointclouds