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

Technological progress increasingly envisions the use of robots interacting with people in
everyday life. Human-robot collaboration (HRC) is the approach that explores the interaction between a human and a robot, during the completion of a common objective, at
the cognitive and physical level. In HRC works, a cognitive model is typically built, which
collects inputs from the environment and from the user, elaborates and translates these into
information that can be used by the robot itself. Machine learning is a recent approach to
build the cognitive model and behavioural block, with high potential in HRC. Consequently,
this paper proposes a thorough literature review of the use of machine learning techniques
in the context of human-robot collaboration. 45 key papers were selected and analysed,
and a clustering of works based on the type of collaborative tasks, evaluation metrics and
cognitive variables modelled is proposed. Then, a deep analysis on different families of machine learning algorithms and their properties, along with the sensing modalities used, is
carried out. Among the observations, it is outlined the importance of the machine learning
algorithms to incorporate time dependencies. The salient features of these works are then
cross-analysed to show trends in HRC and give guidelines for future works, comparing them
with other aspects of HRC not appeared in the review.