  Pruning is the art of cutting unwanted and unhealthy plant branches and is
one of the difficult tasks in the field robotics. It becomes even more complex
when the plant branches are moving. Moreover, the reproducibility of robot
pruning skills is another challenge to deal with due to the heterogeneous
nature of vines in the vineyard. This research proposes a multi-modal framework
to deal with the dynamic vines with the aim of sim2real skill transfer. The 3D
models of vines are constructed in blender engine and rendered in simulated
environment as a need for training the robot. The Natural Admittance Controller
(NAC) is applied to deal with the dynamics of vines. It uses force feedback and
compensates the friction effects while maintaining the passivity of system. The
faster R-CNN is used to detect the spurs on the vines and then statistical
pattern recognition algorithm using K-means clustering is applied to find the
effective pruning points. The proposed framework is tested in simulated and
real environments.

Robotics;Systems and Control;Systems and Control