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This chapter proposes a framework explaining the evolution of property rights in land, assuming two unequal groups of actors: elites possessing means of violence and nonelite land cultivators. It then shows that all intermediary groups – those acting between the chief violence holders (i.e., rulers) and cultivators – are in effect (greater or lesser rulers and cultivators). Using this framework, this chapter explains most of the developments in the evolution of land rights in 19th century colonial Bengal. The proposed theoretical framework explains how different, hierarchically arrayed claims over land and the resulting allocation of rights was a function of asymmetries in power and information between three groups: rulers, direct cultivators, and intermediaries without their own coercive means. It explains inter alia why private property in land was not likely to emerge in this configuration, and that the (non-private) property rights of the other two groups wouldn't attain stability as long as rulers perceived an information asymmetry. In such a situation, land rights would attain neither “private,” nor “public” character.

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.