Voluntary Interaction Detection for Safe Human-Robot Collaboration

In this paper we propose an adaptive algorithm for safe physical human-robot collaboration using admittance control. Our approach adopts tactile sensors as a physical communication channel through which a human can express its intention to the robot. The use of distributed tactile sensors allows to retrieve a rich geometric representation of unpredictable contact events, useful to reconstruct a footprint of the external environment. In particular the shape of a human hand can be retrieved whenever a person touches or grasps a surface covered with tactile sensors. We use hand shape detection to discriminate between voluntary and non-voluntary interaction, thus classifying situations in which the human is deliberately making contact with the robot or an eventual collision is unintended. This method allows to enable robot motion only when the operator intentionally decides to move it, thus avoiding unpredictable behaviors in case of accidental collisions. For this purpose, detection information is used to perform online gain tuning of an admittance controller in order to enforce safety in manual guidance applications. We validate our approach on a Franka Emika 7-dof manipulator, evaluating the algorithm in scenarios where both voluntary and undesired contacts can occur, comparing the proposed method with respect to a basic admittance controller. Through experiments we show how voluntary interaction detection can mitigate the effects of undesired collisions with any of the body parts and could potentially limit harmful situations. A comprehensive video of the experiments is available at the following link: https://youtu.be/C0UeTFudy3M.