%0 Book Section %1 Sung2018 %A Sung, Jaeyong %A Jin, Seok Hyun %A Saxena, Ashutosh %B Robotics Research: Volume 2 %C Cham %D 2018 %E Bicchi, Antonio %E Burgard, Wolfram %I Springer International Publishing %K TODO design paper robotics %P 701--720 %R 10.1007/978-3-319-60916-4_40 %T Robobarista: Object Part Based Transfer of Manipulation Trajectories from Crowd-Sourcing in 3D Pointclouds %U https://doi.org/10.1007/978-3-319-60916-4_40 %X There is a large variety of objects and appliances in human environments, such as stoves, coffee dispensers, juice extractors, and so on. It is challenging for a roboticist to program a robot for each of these object types and for each of their instantiations. In this work, we present a novel approach to manipulation planning based on the idea that many household objects share similarly-operated object parts. We formulate the manipulation planning as a structured prediction problem and design a deep learning model that can handle large noise in the manipulation demonstrations and learns features from three different modalities: point-clouds, language and trajectory. In order to collect a large number of manipulation demonstrations for different objects, we developed a new crowd-sourcing platform called Robobarista. We test our model on our dataset consisting of 116 objects with 249 parts along with 250 language instructions, for which there are 1225 crowd-sourced manipulation demonstrations. We further show that our robot can even manipulate objects it has never seen before. %@ 978-3-319-60916-4

@inbook{Sung2018, abstract = {There is a large variety of objects and appliances in human environments, such as stoves, coffee dispensers, juice extractors, and so on. It is challenging for a roboticist to program a robot for each of these object types and for each of their instantiations. In this work, we present a novel approach to manipulation planning based on the idea that many household objects share similarly-operated object parts. We formulate the manipulation planning as a structured prediction problem and design a deep learning model that can handle large noise in the manipulation demonstrations and learns features from three different modalities: point-clouds, language and trajectory. In order to collect a large number of manipulation demonstrations for different objects, we developed a new crowd-sourcing platform called Robobarista. We test our model on our dataset consisting of 116 objects with 249 parts along with 250 language instructions, for which there are 1225 crowd-sourced manipulation demonstrations. We further show that our robot can even manipulate objects it has never seen before.}, added-at = {2018-05-02T19:59:58.000+0200}, address = {Cham}, author = {Sung, Jaeyong and Jin, Seok Hyun and Saxena, Ashutosh}, biburl = {https://www.bibsonomy.org/bibtex/2fe284aca953f978d89e262b781eb2c03/achakraborty}, booktitle = {Robotics Research: Volume 2}, description = {Robobarista: Object Part Based Transfer of Manipulation Trajectories from Crowd-Sourcing in 3D Pointclouds | SpringerLink}, doi = {10.1007/978-3-319-60916-4_40}, editor = {Bicchi, Antonio and Burgard, Wolfram}, interhash = {67a37a3a97940cf14918af818fd50108}, intrahash = {fe284aca953f978d89e262b781eb2c03}, isbn = {978-3-319-60916-4}, keywords = {TODO design paper robotics}, pages = {701--720}, publisher = {Springer International Publishing}, timestamp = {2018-05-02T19:59:58.000+0200}, title = {Robobarista: Object Part Based Transfer of Manipulation Trajectories from Crowd-Sourcing in 3D Pointclouds}, url = {https://doi.org/10.1007/978-3-319-60916-4_40}, year = 2018 }