Tacit learning for emergence of task-related behavior through signal accumulation
Résumé
Control of robotic joints movements requires generation of appropriate torque and force patterns, coordinating the kinematically and dynamically complex multijoints systems. Control theory coupled with inverse and forward internal models are commonly used to map a desired endpoint trajectory into suitable force patterns. In this paper, we propose the use of tacit learning to successfully achieve similar tasks without using any kinematic model of the robotic system to be controlled. Our objective is to design a new control strategy that can achieve levels of adaptability similar to those observed in living organism and be plausible from a neural control view point. If the neural mechanisms used for mapping goal expressed in the task-space into control-space related commands without using internal models remain largely unknown, many neural systems rely on data accumulation. The presented controller does not use any internal model and incorporates knowledge expressed in the task space using only accumulation of data. Tested on a simulated two links robot system, the controller showed flexibility by developing and updating its parameters through learning. This controller reduces the gap between reflexive motion based on simple accumulation of data and execution of voluntary planned actions in a simple manner that does not require complex analysis of the dynamics of the system.