  Automating a robotic task, e.g., robotic suturing can be very complex and
time-consuming. Learning a task model to autonomously perform the task is
invaluable making the technology, robotic surgery, accessible for a wider
community. The data of robotic surgery can be easily logged where the collected
data can be used to learn task models. This will result in reduced time and
cost of robotic surgery in which a surgeon can supervise the robot operation or
give high-level commands instead of low-level control of the tools. We present
a data-set of needle insertion in soft tissue with two arms where Arm 1 inserts
the needle into the tissue and Arm 2 actively manipulate the soft tissue to
ensure the desired and actual exit points are the same. This is important in
real-surgery because suturing without active manipulation of tissue may yield
failure of the suturing as the stitch may not grip enough tissue to resist the
force applied for the suturing. We present a needle insertion dataset including
60 successful trials recorded by 3 pair of stereo cameras. Moreover, we present
Deep-robot Learning from Demonstrations that predicts the desired state of the
robot at the time step after t (which the optimal action taken at t yields) by
looking at the video of the past time steps, i.e. n step time history where N
is the memory time window, of the task execution. The experimental results
illustrate our proposed deep model architecture is outperforming the existing
methods. Although the solution is not yet ready to be deployed on a real robot,
the results indicate the possibility of future development for real robot
deployment.
