Predictive Compliance For Interaction Control Of Robot Manipulators

This paper presents the use of context-based predictions for the selection and on-line modification of the compliance of a robot manipulator. The work is partially inspired on current neuroscience hypotheses about the control of the human arm and the computational processes used by the brain. 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 a robotic platform by means of forward models and compliance adaption via stiffness control as speculated in neuroscience hypotheses. A second experiment uses context-based predictions to modify on-line the compliance of the robot manipulator. For this task, a Bayesian predictor in the form of a Relevance Vector Machine combines the use of prior knowledge and expected sensory feedback to correct for an erroneous compliance in the case of a falsely-predicted context. The results are combined in an architecture called Predictive Context-Based Adaptive Compliance (PCAC).