  Steerable needles are a promising technology for delivering targeted
therapies in the body in a minimally-invasive fashion, as they can curve around
anatomical obstacles and hone in on anatomical targets. In order to accurately
steer them, controllers must have full knowledge of the needle tip's
orientation. However, current sensors either do not provide full orientation
information or interfere with the needle's ability to deliver therapy. Further,
torsional dynamics can vary and depend on many parameters making steerable
needles difficult to accurately model, limiting the effectiveness of
traditional observer methods. To overcome these limitations, we propose a
model-free, learned-method that leverages LSTM neural networks to estimate the
needle tip's orientation online. We validate our method by integrating it into
a sliding-mode controller and steering the needle to targets in gelatin and ex
vivo ovine brain tissue. We compare our method's performance against an
Extended Kalman Filter, a model-based observer, achieving significantly lower
targeting errors.
