  The game of Jenga represents an inspiring benchmark for developing innovative
manipulation solutions for complex tasks. Indeed, it encouraged the study of
novel robotics methods to successfully extract blocks from the tower. A Jenga
game round undoubtedly embeds many traits of complex industrial or surgical
manipulation tasks, requiring a multi-step strategy, the combination of visual
and tactile data, and the highly precise motion of the robotic arm to perform a
single block extraction. In this work, we propose a novel, cost-effective
architecture for playing Jenga with e.Do, a 6-DOF anthropomorphic manipulator
manufactured by Comau, a standard depth camera, and an inexpensive
monodirectional force sensor. Our solution focuses on a visual-based control
strategy to accurately align the end-effector with the desired block, enabling
block extraction by pushing. To this aim, we train an instance segmentation
deep learning model on a synthetic custom dataset to segment each piece of the
Jenga tower, allowing visual tracking of the desired block's pose during the
motion of the manipulator. We integrate the visual-based strategy with a 1D
force sensor to detect whether the block can be safely removed by identifying a
force threshold value. Our experimentation shows that our low-cost solution
allows e.DO to precisely reach removable blocks and perform up to 14
consecutive extractions in a row.
