{"type":"ScholarlyArticle","title":"Graph-Based Inverse Optimal Control for Robot Manipulation","authors":[{"name":"Arunkumar Byravan","slug":"arunkumar-byravan","given":"Arunkumar","family":"Byravan","affiliation":""},{"name":"Mathew Monfort","slug":"mathew-monfort","given":"Mathew","family":"Monfort","affiliation":""},{"name":"Brian D. Ziebart","slug":"brian-d-ziebart","given":"Brian D.","family":"Ziebart","affiliation":""},{"name":"Byron Boots","slug":"byron-boots","given":"Byron","family":"Boots","affiliation":""},{"name":"Dieter Fox","slug":"dieter-fox","given":"Dieter","family":"Fox","affiliation":""}],"abstract":"Inverse optimal control (IOC) is a powerful approach for learning robotic controllers from demonstration that estimates a cost function which rationalizes demonstrated control trajectories. Unfortunately, its applicability is difficult in settings where optimal control can only be solved approximately. While local IOC approaches have been shown to successfully learn cost functions in such settings, they rely on the availability of good reference trajectories, which might not be available at test time. We address the problem of using IOC in these computationally challenging control tasks by using a graph-based discretization of the trajectory space. Our approach projects continuous demonstrations onto this discrete graph, where a cost function can be tractably learned via IOC. Discrete control trajectories from the graph are then projected back to the original space and locally optimized using the learned cost function. We demonstrate the effectiveness of the approach with experiments conducted on two 7-degree of freedom robotic arms.","doi":null,"url":"https://ijcai.org/proceedings/2015/266","pdf":"https://ijcai.org/proceedings/2015/0266.pdf","markdown":"https://ijcai.org/proceedings/2015/266.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/266","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Robotics and Vision","track":"Main Track","number":266,"pages":{"first":1874,"last":1879},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}