Passivity and Learnability for Mechanical Systems--A Learning Control Theory for Skill Refinement--

Suguru ARIMOTO  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E75-A   No.5   pp.552-560
Publication Date: 1992/05/25
Online ISSN: 
Print ISSN: 0916-8508
Type of Manuscript: INVITED PAPER (Special Section on Nonlinear Dynamics--Adaptive, Learning and Neural Systems--)
learning control,  skill refinement,  passivity,  learnability,  robot manipulators,  

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This paper attempts to account for intelligibility of practices-based learning (so-called 'learning control') for skill refinement from the viewpoint of Newtonian mechanics. It is shown from an axiomatic approach that an extended notion of passivity for the residual error dynamics of robots plays a crucial role in their ability of learning. More precisely, it is shown that the exponentially weighted passivity with respect to residual velocity vector and torque vector leads the robot system to the convergence of trajectory tracking errors to zero with repeating practices. For a class of tasks when the endpoint is constrained geometrically on a surface, the problem of convergence of residual tracking errors and residual contact-force errors is also discussed on the basis of passivity analysis.