Incremental adaptation of a robot body schema based on touch events

The term 'body schema' refers to a computational representation of a physical body; the neural representation of a human body, or the numerical representation of a robot body. In both humans and robots, such a representation is crucial to accurately control body movements. While humans learn and continuously adapt their body schema based on multimodal perception and neural plasticity, robots are typically assigned with a fixed analytical model (e.g., the robot kinematics) which describes their bodies. However, there are always discrepancies between a model and the real robot, and they vary over time, thus affecting the accuracy of movement control. In this work, we equip a humanoid robot with the ability to incrementally estimate such model inaccuracies by touching known planar surfaces (e.g., walls) in its vicinity through motor babbling exploration, effectively adapting its own body schema based on the contact information alone. The problem is formulated as an adaptive parameter estimation (Extended Kalman Filter) which makes use of planar constraints obtained at each contact detection. We compare different incremental update methods through an extensive set of experiments with a realistic simulation of the iCub humanoid robot, showing that the model inaccuracies can be reduced by more than 80%.