Online interactive perception of articulated objects with multi-level recursive estimation based on task-specific priors

To successfully manipulate in unknown environments, a robot must be able to perceive degrees of freedom of objects in its environment. Based on the resulting kinematic model and joint configurations, the robot is able to select and adapt actions, recognize their successful completion and detect failure. We present an RGB-D-based online algorithm for the interactive perception of articulated objects. The algorithm decomposes the perception problem into three interconnected levels of recursive estimation. The estimation problems at each level are much simpler than the original problem and their robustness is improved by level-specific priors that help reject noise in the measurements. These three estimators mutually inform each other to further improve the convergence properties of the three estimation solutions. We demonstrate that the resulting algorithm is robust, accurate, and versatile in real-world experiments. We also show how the perceptual skill can be used online to control the robot's behavior in real-world manipulation tasks.