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It requires reasoning about the desired effect of the task and, thus, properly grasping and manipulating the tool to achieve the task. Most work in robotics has focused on task-agnostic grasping, which optimizes for only grasp robustness without considering the subsequent manipulation tasks. In this article, we propose the Task-Oriented Grasping Network (TOG-Net) to jointly optimize both task-oriented grasping of a tool and the manipulation policy for that tool. The training process of the model is based on large-scale simulated self-supervision with procedurally generated tool objects. We perform both simulated and real-world experiments on two tool-based manipulation tasks: sweeping and hammering. Our model achieves overall 71.1% task success rate for sweeping and 80.0% task success rate for hammering.<\/jats:p>","DOI":"10.1177\/0278364919872545","type":"journal-article","created":{"date-parts":[[2019,8,29]],"date-time":"2019-08-29T08:52:26Z","timestamp":1567068746000},"page":"202-216","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":146,"title":["Learning task-oriented grasping for tool manipulation from simulated self-supervision"],"prefix":"10.1177","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7620-3117","authenticated-orcid":false,"given":"Kuan","family":"Fang","sequence":"first","affiliation":[{"name":"Stanford University, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuke","family":"Zhu","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Animesh","family":"Garg","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, CA, USA"},{"name":"Nvidia, Santa Clara, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrey","family":"Kurenkov","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Viraj","family":"Mehta","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Fei-Fei","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silvio","family":"Savarese","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2019,8,29]]},"reference":[{"key":"bibr1-0278364919872545","doi-asserted-by":"publisher","DOI":"10.1201\/9781420024203"},{"key":"bibr2-0278364919872545","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2013.2289018"},{"key":"bibr3-0278364919872545","author":"Bousmalis K","year":"2017","journal-title":"arXiv preprint arXiv:1709.07857"},{"key":"bibr4-0278364919872545","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8460875"},{"key":"bibr5-0278364919872545","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-1428-6_1652"},{"key":"bibr6-0278364919872545","doi-asserted-by":"publisher","DOI":"10.1177\/0278364909105606"},{"key":"bibr7-0278364919872545","unstructured":"Coumans E, Bai Y (2016) PyBullet, a Python module for physics simulation, games, robotics and machine learning. 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