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Using large datasets in machine learning has led to outstanding results, insome cases outperforming humans in tasks that were believed impossible formachines. However, achieving human-level performance when dealing withphysically interactive tasks, e.g., in contact-rich robotic manipulation, isstill a big challenge. It is well known that regulating the Cartesian impedancefor such operations is of utmost importance for their successful execution.Approaches like reinforcement Learning (RL) can be a promising paradigm forsolving such problems. More precisely, approaches that use task-agnostic expertdemonstrations to bootstrap learning when solving new tasks have a hugepotential since they can exploit large datasets. However, existing datacollection systems are expensive, complex, or do not allow for impedanceregulation. This work represents a first step towards a data collectionframework suitable for collecting large datasets of impedance-based expertdemonstrations compatible with the RL problem formulation, where a novel actionspace is used. The framework is designed according to requirements acquiredafter an extensive analysis of available data collection frameworks forrobotics manipulation. The result is a low-cost and open-access tele-impedanceframework which makes human experts capable of demonstrating contact-richtasks.

An Open Tele-Impedance Framework to Generate Data for Contact-Rich Tasks in Robotic Manipulation