Online Body Schema Adaptation Based on Internal Mental Simulation and Multisensory Feedback

In this paper, we describe a novel approach to obtain automatic adaptation of the robot body schema and to improve the robot perceptual and motor skills based on this body knowledge. Predictions obtained through a mental simulation of the body are combined with the real sensory feedback to achieve two objectives simultaneously: body schema adaptation and markerless 6D hand pose estimation. The body schema consists of a computer graphics simulation of the robot, which includes the arm and head kinematics (adapted online during the movements) and an appearance model of the hand shape and texture. The mental simulation process generates predictions on how the hand will appear in the robot camera images, based on the body schema and the proprioceptive information (i.e. motor encoders). These predictions are compared to the actual images using Sequential Monte Carlo techniques to feed a particle-based Bayesian estimation method to estimate the parameters of the body schema. The updated body schema will improve the estimates of the 6D hand pose, which is then used in a closed-loop control scheme (i.e. visual servoing), enabling precise reaching. We report experiments with the iCub humanoid robot that support the validity of our approach. A number of simulations with precise ground-truth were performed to evaluate the estimation capabilities of the proposed framework. Then, we show how the use of high-performance GPU programming and an edge-based algorithm for visual perception allow for real-time implementation in real world scenarios.