An approach to simplify the learning space for robot learning control

Although learning controllers are considered to be capable of generalization, most robot learning control schemes either need to include conventional controllers, or need to repeat the learning process each time a new trajectory is encountered. The main reason for this deficiency is that the learning space for executing general motions of multi-joint robot manipulators is too large. In this paper, we propose an approach, motivated by the equilibrium-point hypothesis in human motor control, to simplify the learning space when learning controllers are used to govern robot motions. In the proposed approach, the motion command is formulated in the form of three square pulses in alternate directions with adjustable heights and widths. When the motion command is specified in this simple form, the learning space for dealing with variations exhibited in different movements is dramatically simplified. Thus, we can then implement a fuzzy system for robot motion control, which generates appropriate controlled parameters for the motion commands by using a reasonable number of rules. Theoretical analyses and simulations are performed to demonstrate the feasibility of the proposed approach.