3. Learning Kinematic Models of Articulated Objects
author: Kurt Konolige, Artificial Intelligence Center, SRI
author: Christian Plagemann, Computer Science Department, Stanford University
author: Cyril Stachniss, Department of Computer Science, University of Freiburg
author: Vijay Pradeep, Stanford University
author: Jürgen Sturm, Department of Computer Science, University of Freiburg
published: June 20, 2009, recorded: May 2009, views: 262
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Robots operating in home environments must be able to interact with articulated objects such as doors or drawers. Ideally, robots are able to autonomously infer articulation models by observation. In this video, we briefly present an approach for learning kinematic models by inferring the connectivity of rigid parts and the articulation models for the corresponding links. Our method uses a mixture of parameterized and parameter-free (Gaussian process) representations and finds low-dimensional manifolds that provide the best explanation of the given observations. Our approach has been implemented and evaluated using real data obtained in various realistic home environment settings. Corresponding paper: http://www.informatik.uni-freiburg.de/~sturm/media/sturm09ijcai.pdf
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !