  In this paper, we present a simple framework of skill transfer learning for
robotic ultrasound-guidance procedures. We briefly review challenges in skill
transfer learning for robotic ultrasound-guidance procedures. We then identify
the need of appropriate sampling techniques, computationally efficient neural
networks models that lead to the proposal of a simple framework of skill
transfer learning for real-time applications in robotic ultrasound-guidance
procedures. We present pilot experiments from two participants (one experienced
clinician and one non-clinician) looking for an optimal scanning plane of the
four-chamber cardiac view from a fetal phantom. We analysed ultrasound image
frames, time series of texture image features and quaternions and found that
the experienced clinician performed the procedure in a quicker and smoother way
compared to lengthy and non-constant movements from non-clinicians. For future
work, we pointed out the need of pruned and quantised neural network models for
real-time applications in robotic ultrasound-guidance procedure. The resources
to reproduce this work are available at
\url{https://github.com/mxochicale/rami-icra2023}.
