High-quality information set abstraction remains a core challenge in solving large-scale imperfect-information extensive-form games (IIEFGs)--such as no-limit Texas Hold’em--where the finite nature of spatial resources hinders solving strategies for the full game. State-of-the-art AI methods rely on pre-trained discrete clustering for abstraction, yet their hard classification irreversibly discards critical information: specifically, the quantifiable subtle differences between information sets--vital for strategy solving--thus compromising the quality of such solving. Inspired by the word embedding paradigm in natural language processing, this paper proposes the Embedding CFR algorithm, a novel approach for solving strategies in IIEFGs within an embedding space. The algorithm pre-trains and embeds the features of individual information sets into an interconnected low-dimensional continuous space, where the resulting vectors more precisely capture both the distinctions and connections between information sets. Embedding CFR introduces a strategy-solving process driven by regret accumulation and strategy updates in this embedding space, with supporting theoretical analysis verifying its ability to reduce cumulative regret. Experiments on poker show that with the same spatial overhead, Embedding CFR achieves significantly faster exploitability convergence compared to cluster-based abstraction algorithms, confirming its effectiveness. Furthermore, to our knowledge, it is the first algorithm in poker AI that pre-trains information set abstractions via low-dimensional embedding for strategy solving.

Physical human–robot interaction performance of present rehabilitation robots are still not satisfactory in the clinical practice. Especially, the work space where the robot can be driven smoothly by users is still very limited, which prevents rehabilitation robots from being applied successfully. In this study, a new concept of drivable space is proposed to evaluate the work spaces of rehabilitation robots, and a method for expanding the drivable space is designed based on the dynamics of the coupled human–robot system and human joint characteristics. First, the definition of drivable space is presented based on comparison of human joint torques, and the minimal torques necessary to drive robot joints, which is mainly determined by the torque estimation errors for general rehabilitation robots driven smoothly by motors. Therefore, a method for improving torque estimation accuracies based on dynamics modeling is then designed. A data-driven error prediction method based on Gaussian process regression is proposed to adaptively compensate the model errors, by which the most accurate dynamic model so far for the coupled system can be obtained, and a method for generation of the training dataset, which is used in error prediction, is designed as well. Moreover, the torque–angle relationship of human joints is modeled and used to optimize the torque error distribution, by which it can be proven that the drivable space can be further expanded. Finally, performance of the proposed methods are demonstrated and validated by experiments carried out on a lower limb rehabilitation robot.