Experimental Force-Torque Dataset for Robot Learning of Multi-Shape Insertion
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Most real-world systems are complex and hard to model accurately. Machine learning has been used to model complex dynamical systems (e.g. articulated robot structures, cable stretch) or coupled with reinforcement learning to learn new tasks based on vision and position sensors (e.g. grasping, reaching). To solve complex tasks using machine learning techniques, availability of a suitable dataset is an important factor. The robotic community still lacks public datasets, especially for problems that are complex to model like contact tasks, where it is difficult to obtain a precise model of the physical interaction between two objects. In this paper, we provide a public dataset for insertion of convex-shaped pegs in holes and analyze the nature of the task. We demonstrate using the data how a robot learns to insert polyhedral pegs into holes using only a 6-axis force/torque sensor. This dataset can also be used to learn other contact tasks such as shape recognition.
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