  Tendon-driven continuum robots have been gaining popularity in medical
applications due to their ability to curve around complex anatomical
structures, potentially reducing the invasiveness of surgery. However, accurate
modeling is required to plan and control the movements of these flexible
robots. Physics-based models have limitations due to unmodeled effects, leading
to mismatches between model prediction and actual robot shape. Recently
proposed learning-based methods have been shown to overcome some of these
limitations but do not account for hysteresis, a significant source of error
for these robots. To overcome these challenges, we propose a novel deep decoder
neural network that predicts the complete shape of tendon-driven robots using
point clouds as the shape representation, conditioned on prior configurations
to account for hysteresis. We evaluate our method on a physical tendon-driven
robot and show that our network model accurately predicts the robot's shape,
significantly outperforming a state-of-the-art physics-based model and a
learning-based model that does not account for hysteresis.
