DLO-in-hole for assembly tasks with tactile feedback and LSTM networks

In this paper, a tactile-based robotic system to perform the insertion of a Deformable Linear Object (DLO) in a hole is proposed. This is a typical application in manufacturing processes involving assembly of electric cables in connectors or electromechanical components. A Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM) cells is adopted in this work to predict the external forces acting on the DLO from the tactile data. The tactile sensor, mounted on the finger, provides 16 signals and it is the only sensor required during the effective insertion task. In a real environment, the tight spaces very often prevent the possibility to use the vision system, also when the same task is performed by a human being. Force/Torque sensors instead increase the system price and provide signals that might be affected by inertia disturbance or other undesired effect, that are difficult to manage. The control law design is based on the RNN outputs and the distance between end-effector and target hole. In particular, it leads the plastically deformed DLO inside the hole while it adjusts the tool pose guided by the control errors in order to prevent buckling. The contribute of this work is dual: first a tactile feedback integrating a RNN to estimate contact forces on the grasped object is presented; second, a control system is developed to perform a challenging insertion of a DLO in a hole. Experimental works are presented to validate the proposed algorithms.