Autonomous Learning of the Robot Kinematic Model

Robotics systems are becoming more and more autonomous and reconfigurable. In this context, the design of algorithms capable of deriving kinematics and dynamics models directly from data could be particularly useful. In this article, we present an algorithm that learns a forward kinematics model of a robot starting from a time series of visual observations. Our strategy can be applied to any robot with serial kinematics composed of revolute and prismatics joints. First, the algorithm identifies the robot kinematic structure, i.e., a high-level description of the robot geometry that defines the connections between the rigid-bodies composing the robot. Then, the algorithm derives the forward kinematics relying on a Gaussian process (GP) model. More precisely, the GP model is based on a polynomial kernel, defined exploiting the kinematic structure previously identified. The effectiveness of the proposed solution has been tested via extensive Monte Carlo simulations, as well as via experiments on a real UR10 robot.