{"type":"ScholarlyArticle","title":"Learning to Walk through Imitation","authors":[{"name":"Rawichote Chalodhorn","slug":"rawichote-chalodhorn","given":"Rawichote","family":"Chalodhorn","affiliation":""},{"name":"David B. Grimes","slug":"david-b-grimes","given":"David B.","family":"Grimes","affiliation":""},{"name":"Keith Grochow","slug":"keith-grochow","given":"Keith","family":"Grochow","affiliation":""},{"name":"Rajesh P. N. Rao","slug":"rajesh-p-n-rao","given":"Rajesh P. N.","family":"Rao","affiliation":""}],"abstract":"Programming a humanoid robot to walk is a chal-lenging problem in robotics. Traditional ap-proaches rely heavily on prior knowledge of the robot's physical parameters to devise sophisticated control algorithms for generating a stable gait. In this paper, we provide, to our knowledge, the first demonstration that a humanoid robot can learn to walk directly by imitating a human gait obtained from motion capture (mocap) data. Training using human motion capture is an intuitive and flexible approach to programming a robot but direct usage of mocap data usually results in dynamically un-stable motion. Furthermore, optimization using mocap data in the humanoid full-body joint-space is typically intractable. We propose a new model-free approach to tractable imitation-based learning in humanoids. We represent kinematic information from human motion capture in a low dimensional subspace and map motor commands in this low-dimensional space to sensory feedback to learn a predictive dynamic model. This model is used within an optimization framework to estimate op-timal motor commands that satisfy the initial kine-matic constraints as best as possible while at the same time generating dynamically stable motion. We demonstrate the viability of our approach by providing examples of dynamically stable walking in a humanoid learned from mocap data using both a dynamic simulator and a real humanoid robot.","doi":null,"url":"https://ijcai.org/proceedings/2007/336","pdf":"https://ijcai.org/proceedings/2007/0336.pdf","markdown":"https://ijcai.org/proceedings/2007/336.md","bibtex":"https://ijcai.org/proceedings/2007/bibtex/336","volume":{"slug":"2007","title":"Proceedings of the Twentieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2007","url":"https://ijcai.org/proceedings/2007/","year":2007,"location":"Hyderabad, India","dates":"","editors":"Ramon López de Mántaras","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":336,"pages":{"first":2084,"last":2089},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}