Bio-inspired Control Model for Object Manipulation by Humanoid Robots

This paper presents a bio-inspired control model for humanoid robots manipulating objects. Humanoids face several genuine problems: 1) they are not fixed (to the ground) therefore extreme forces generate noisy vibrations on the whole platform (robot body) and 2) rigid control (to avoid dynamic modelling) requires high power to be accurate and dramatically limits their autonomy.

We compare a velocity vs a position driven control scheme in the framework of object manipulation. The velocity driven control scheme helps smoother control (reducing the jerks). Furthermore, we use an artificial neural network (RBF) to extract some features of the dynamic model automatically complementing the control scheme.

Its performance is evaluated using a real robot platform. Experiments were done using the robot’s arm and trajectory data was collected during different trials manipulating different objects in order to acquire the model and evaluate how to use it to improve control accuracy.