Understanding robot motor capability using information-theory-based approach

Robot skills are usually learned from the so-called learning-from-human-demonstration methods. However, with the limitation of robot motor capability, a robot may not be able to duplicate human motor skills with the same motor performance. To alleviate the problem, one of the possible solutions is to know robot motor capability in advance. Thus, we develop a quantitative measure of a robot motor system, called a pseudo index of motor performance (pI_p), and utilize it to compare with the index of performance (I_p) of a human motor system. To investigate the I_p, we propose an information-theory-based method to characterize a robot motor system. Computer simulations and experiments with a PUMA 560 robot will be conducted to validate the proposed information-theory-based method.