Human behaviour and intention prediction for industrial human-robot collaboration: Potentials, challenges, and opportunities

The behaviour of humans when collaborating with other actors such as other humans or robots has been studied for decades and remains critical for unlocking mixed human-robot team performance. To achieve better, more effective human-robot collaboration (HRC) in industrial settings, a robot needs to have robust models to predict human behaviour when working towards a shared goal. Inferring and modelling human intentions is expected to play a pivotal role in accurately predicting human behaviour as suggested in this paper. This paper explores the potentials, challenges, and opportunities in human behaviour and intention prediction for industrial HRC. It first examines human factors in socio-technical systems, highlighting the challenges of human-human collaboration that inform the design of human-robot teams. Building on this foundation, a human behaviour and intention prediction framework is introduced that enables seamless HRC by integrating behavioural modelling techniques. The paper further reviews perception systems used to interpret the physical world and decode physiological and affective states, along with reasoning and planning strategies based on probabilistic, rule-based, machine learning, hybrid, and language model approaches. Verification and validation methodologies are discussed to ensure reliability and safety, followed by an analysis of the ethical, legal, and societal implications of predictive modelling in industrial HRC. Finally, the paper concludes with a discussion of open challenges and future research directions, emphasising advancements in adaptive modelling, perception-driven reasoning, and ethical frameworks to shape the next generation of collaborative robots that are robust, human-centric, and ethically responsible. Data Availability No new data were generated or analysed in this study.