Object-Based Visual 3D Tracking of Articulated Objects via Kinematic Sets
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1 - Volume 01
Automatic Kinematic Chain Building from Feature Trajectories of Articulated Objects
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Skeletal parameter estimation from optical motion capture data
SIGGRAPH '04 ACM SIGGRAPH 2004 Sketches
Unsupervised Learning of Skeletons from Motion
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Learning forward models for robots
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A probabilistic framework for learning kinematic models of articulated objects
Journal of Artificial Intelligence Research
Learning probabilistic models for mobile manipulation robots
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Occlusion-aware multi-view reconstruction of articulated objects for manipulation
Robotics and Autonomous Systems
Hi-index | 0.00 |
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 paper, we present an approach to learn 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.