A study on position control of continuum arm using MAML(Model-Agnostic
Meta-Learning) for adapting changing conditions
Non linear deformation of spring based continuum manipulators cause
difficulty in predicting the position of the tip. When we put different
tools on the tip for various purposes, the difficulty further increases.
Model less control of the manipulator has shown great success in tip
positioning of these types of manipulators. One of the major drawbacks
of model less control is the requirement of a large data set and time.
Hence, this paper studies the effect of the implementation of
MAML(Model-Agnostic Meta-Learning) for fast adaptation of different
offset conditions. The effects have been studied in the simulation
environment and on the real prototype. The continuum arm used for the
experimentation is a tendon based non constant curvature spring based
manipulator. An average error of 0.03m has been achieved on the
prototype. MAML was successful in bringing down the relative tip
positioning error of the manipulator from 7.02% to 1.55% in the
simulation environment. It also showed success in bringing down the
relative tip positioning error from 11.06% to 4.09% on real prototype.
We also studied the effectiveness of the same in trajectory following.