A Probabilistic Framework for Learning Kinematic Models of Articulated Objects
Robots operating in domestic environments generally need to interact with articulated objects, such as doors, cabinets, dishwashers or fridges. In this work, we present a novel, probabilistic framework for modeling articulated objects as kinematic graphs. Vertices in this graph correspond to object parts, while edges between them model their kinematic relationship. In particular, we present a set of parametric and non-parametric edge models and how they can be estimated robustly from noisy pose observations. We furthermore describe how to estimate the kinematic structure and how to use the learned kinematic models for pose prediction and for robotic manipulation tasks. We finally present how the learned models can be generalized to new and previously unseen objects. In various experiments using real robots and household objects, different camera systems as well as in simulation, we show that our approach is valid, accurate and efficient, and has a broad set of applications, in particular for the emerging fields of mobile manipulation and service robotics.