Towards Understanding Articulated Objects

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 representations. To obtain parameter-free models, we seek for low-dimensional manifolds of latent action variables in order to provide the best explanation of the given observations. The mapping from the constrained manifold of an articulated link to the work space is learned by means of Gaussian process regression. Our approach has been implemented and evaluated using real data obtained in various home environment settings. Finally, we discuss the limitations and possible extensions of the proposed method.

[1]  Neil D. Lawrence,et al.  Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models , 2005, J. Mach. Learn. Res..

[2]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[3]  Trevor Darrell,et al.  Recovering Articulated Model Topology from Observed Rigid Motion , 2002, NIPS.

[4]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[5]  Richard S. Zemel,et al.  Unsupervised Learning of Skeletons from Motion , 2008, ECCV.

[6]  Wolfram Burgard,et al.  Unsupervised body scheme learning through self-perception , 2008, 2008 IEEE International Conference on Robotics and Automation.

[7]  Wolfram Burgard,et al.  Adaptive Body Scheme Models for Robust Robotic Manipulation , 2008, Robotics: Science and Systems.

[8]  Marc Pollefeys,et al.  Automatic Kinematic Chain Building from Feature Trajectories of Articulated Objects , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[9]  Yiannis Demiris,et al.  Learning Forward Models for Robots , 2005, IJCAI.

[10]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[11]  Wolfram Burgard,et al.  Learning Kinematic Models for Articulated Objects , 2009, IJCAI.

[12]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[13]  Maja J. Mataric,et al.  Markerless kinematic model and motion capture from volume sequences , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[14]  Oliver Brock,et al.  Learning to Manipulate Articulated Objects in Unstructured Environments Using a Grounded Relational Representation , 2008, Robotics: Science and Systems.

[15]  Deva Ramanan,et al.  Learning to parse images of articulated bodies , 2006, NIPS.

[16]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[17]  Björn Johansson,et al.  Comparison of local image descriptors for full 6 degree-of-freedom pose estimation , 2009, 2009 IEEE International Conference on Robotics and Automation.

[18]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[19]  Éric Marchand,et al.  Object-Based Visual 3D Tracking of Articulated Objects via Kinematic Sets , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[20]  Odest Chadwicke Jenkins,et al.  Neighborhood denoising for learning high-dimensional grasping manifolds , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[21]  David A. Forsyth,et al.  Skeletal parameter estimation from optical motion capture data , 2004, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[22]  Siddhartha S. Srinivasa,et al.  Object recognition and full pose registration from a single image for robotic manipulation , 2009, 2009 IEEE International Conference on Robotics and Automation.