Learning Null Space Projections Fast

TY - CHAP

T1 - Learning Null Space Projections Fast

AU - Manavalan, Jeevan

AU - Howard, Matthew Jacob William

PY - 2017

Y1 - 2017

N2 - Typically robot interactions with the environment may involve some type of constraint which impedes the motion of the system. This paper proposes an approach to learn kinematic constraints from observed movements. Our method derives the null space projection of a kinematically constrained system using gradient descent. Moreover, we compare this method to the existing brute force-based approach for learning constraints on data sets of different dimensionality, to demonstrate how it can learn constraints from data sets of a much higher dimensionality.

AB - Typically robot interactions with the environment may involve some type of constraint which impedes the motion of the system. This paper proposes an approach to learn kinematic constraints from observed movements. Our method derives the null space projection of a kinematically constrained system using gradient descent. Moreover, we compare this method to the existing brute force-based approach for learning constraints on data sets of different dimensionality, to demonstrate how it can learn constraints from data sets of a much higher dimensionality.

M3 - Conference paper

BT - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

ER -