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The purpose of this paper is to investigate the influence of mentoring functions on expatriate voice in multinational enterprises and whether job security plays a mediating role in this relationship.

In total, 300 questionnaires were distributed in this study. Of the 173 responses received, 8 invalid questionnaires were excluded and 165 valid questionnaires were analysed. The effective questionnaire recovery rate was 55.00%.

The results revealed that the stronger the psychosocial support function, the role modelling function and the career development provided by the mentor, the more would be the expatriate voice behaviour. Furthermore, the psychological support, role model characteristics and career development guidance affect the expatriate voice behaviour through the mediation of job security.

Few studies have focussed on the influence of expatriate mentoring functions and job security on expatriate voice. Furthermore, whether the mentoring function affects the job security of expatriates is unknown. The objective of this study is to fill this gap in the literature.

Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.