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Technological progress increasingly envisions the use of robots interactingwith people in everyday life. Human-robot collaboration (HRC) is the approachthat explores the interaction between a human and a robot, during thecompletion of a common objective, at the cognitive and physical level. In HRCworks, a cognitive model is typically built, which collects inputs from theenvironment and from the user, elaborates and translates these into informationthat can be used by the robot itself. Machine learning is a recent approach tobuild the cognitive model and behavioural block, with high potential in HRC.Consequently, this paper proposes a thorough literature review of the use ofmachine learning techniques in the context of human-robot collaboration. 45 keypapers were selected and analysed, and a clustering of works based on the typeof collaborative tasks, evaluation metrics and cognitive variables modelled isproposed. Then, a deep analysis on different families of machine learningalgorithms and their properties, along with the sensing modalities used, iscarried out. Among the observations, it is outlined the importance of themachine learning algorithms to incorporate time dependencies. The salientfeatures of these works are then cross-analysed to show trends in HRC and giveguidelines for future works, comparing them with other aspects of HRC notappeared in the review.

Human-robot collaboration and machine learning: a systematic review of recent research