A multimodal dataset for robotic peg extraction based on Bioin-Tacto sensor modules

Robots need to adapt to the complexities of acting in unstructured environments. Robotic systems need more awareness of the environment to adapt to uncertainty and variability. Although cameras have been predominantly used in robotic tasks, the limitations that come with them, such as occlusion, visibility, and lack of information, have diverted some focus to tactile sensing. Extensive datasets of the physical interactions between tactile-enabled robots are required to investigate and develop methods for performing manipulation and object exploration tasks. Therefore, this motivates us to compose a dataset of signals from Bioin-Tacto modules mounted on a robotic gripper performing extraction tasks. An operator controls a robotic gripper to extract three pegs of various complexities from their corresponding holes. This dataset contains angular velocity, linear acceleration, magnetic field intensity and direction, and pressure exerted on two tactile modules embedded in the compliant structure of the sensing module. The dataset comprises 96 extraction episodes, including data collected from a reinforcement learning agent. The dataset can be used to pre-train a reinforcement machine learning model to perform peg-in-hole tasks and to study how pretraining affects a manipulator's ability to infer tactile signals and improve the success rates of the manipulator.