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.