Direct Imitation Learning-based Visual Servoing using the Large Projection Formulation
arxiv(2024)
Abstract
Today robots must be safe, versatile, and user-friendly to operate in
unstructured and human-populated environments. Dynamical system-based imitation
learning enables robots to perform complex tasks stably and without explicit
programming, greatly simplifying their real-world deployment. To exploit the
full potential of these systems it is crucial to implement closed loops that
use visual feedback. Vision permits to cope with environmental changes, but is
complex to handle due to the high dimension of the image space. This study
introduces a dynamical system-based imitation learning for direct visual
servoing. It leverages off-the-shelf deep learning-based perception backbones
to extract robust features from the raw input image, and an imitation learning
strategy to execute sophisticated robot motions. The learning blocks are
integrated using the large projection task priority formulation. As
demonstrated through extensive experimental analysis, the proposed method
realizes complex tasks with a robotic manipulator.
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