This work presents preliminary ideas and experiments on applying current neuroscience hypotheses about bimanual coordination and control using a robotic dual-arm system. A first experiment uses inspiration from the classical neuroscience experiment of the Waiter Task. In the original experiment, the non-dominant human arm is holding a weight of 1 Kg. When this weight is unloaded by a self-generated action (with the dominant arm), it is observed that the non-dominant arm does not suffer perceptible postural changes. The reason arguably stems from the prediction of the forces occurring at the unloading, since the inherently delayed sensory feedback present in biological systems would not suffice to react in such a short notice as observed. The experiment is reproduced in our robotic platform by means of forward models and compliance adaption via stiffness control as speculated in neuroscience hypotheses. A second proposed scenario deals with the estimation of discrete changes on the robot's context by using Bayesian prediction, a theory of increasing popularity among neuroscientists. For this task, a Bayesian estimator in the form of a Relevance Vector Machine combines the use of prior knowledge and sensory feedback to correct for erroneous actions due to falsely-predicted contexts.
