Learning-based Contact Status Recognition for Peg-in-Hole Assembly

Opening a lock without vision sensors remains a challenge for robots. Inspired by the ability of a human to open a lock through touch and intuition, a peg-in-hole assembly method for recognizing the relative position and inclination angle of a hole is proposed. We use supervised learning to generate a contact-state model to judge the relative contact state and introduce force control strategies that ensure stable and safe interaction with the environment. Adaptive impedance control is adopted to ensure the stability of the alignment and insertion process. The proposed method is not restricted by the object shape. The system can learn an effective classification model with a small volume of force and torque data and predict the relative contact state of a peg and hole. The proposed method is verified in an experiment in which a bicycle lock is opened at different inclination angles. The proposed method has potential application in the field of industrial assembly.