R obot L earning M echanisms and F rontier P aradigms u nder the D ual C hallenges of F ew- S hot L earning and S parse R ewards

The amalgamation of Embodied AI with physical robot learning constitutes a pivotal route toward the realization of Artificial General Intelligence (AGI). Robot learning has important application value in real complex tasks. However, the interweaving of extremely high interactive sampling cost and sparse feedback in the real physical world constitutes a "double curse" that hinders robot autonomous skill learning. In the existing research on robot learning, there are a large number of reviews on Robot Learning under few-shot and sparse rewards. However, few scholars have systematically reviewed Robot Learning under the dual effects of few-shot and sparse rewards. This essay systematically discusses the technical trajectory and paradigm shift of solving this dual difficulty. Analyzes the failure mechanism of traditional reinforcement learning (RL) under few-shot and sparse rewards. Sorted out solutions based on three directions: Rapid Generalization (Meta RL, Few-Shot IL), Intrinsic Exploration (HER, Intrinsic Motivation), and Cognitive World Modeling (World Models, Offline RL, VLA embodied model). Specifically, Rapid Generalization focuses on addressing the few-shot learning problem, Intrinsic Exploration targets the sparse reward challenge, and Cognitive World Modeling aims to jointly resolve both issues through integration. In addition, this essay also constructs a systematic taxonomy comparison framework and looks forward to future research directions such as security exploration and embodied alignment.