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

Suguru ARIMOTO

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

Suguru ARIMOTO

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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.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E75-A No.5 pp.552-560

Publication Date: 1992/05/25

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Type of Manuscript: Special Section INVITED PAPER (Special Section on Nonlinear Dynamics--Adaptive, Learning and Neural Systems--)

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Suguru ARIMOTO, "Passivity and Learnability for Mechanical Systems--A Learning Control Theory for Skill Refinement--" in IEICE TRANSACTIONS on Fundamentals, vol. E75-A, no. 5, pp. 552-560, May 1992, doi: .
Abstract: 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e75-a_5_552/_p

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@ARTICLE{e75-a_5_552,
author={Suguru ARIMOTO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Passivity and Learnability for Mechanical Systems--A Learning Control Theory for Skill Refinement--},
year={1992},
volume={E75-A},
number={5},
pages={552-560},
abstract={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.},
keywords={},
doi={},
ISSN={},
month={May},}

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TY - JOUR
TI - Passivity and Learnability for Mechanical Systems--A Learning Control Theory for Skill Refinement--
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 552
EP - 560
AU - Suguru ARIMOTO
PY - 1992
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E75-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 1992
AB - 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.
ER -