Pre-trained models trained with internet-scale data have achieved significant improvements in perception, interaction, and reasoning. Using them as the basis of embodied grasping methods has greatly promoted the development of robotics applications. In this paper, we provide a comprehensive review of the latest developments in this field. First, we summarize the embodied foundations, including cutting-edge embodied robots, simulation platforms, publicly available datasets, and data acquisition methods, to fully understand the research focus. Then, the embodied algorithms are introduced, starting from pre-trained models, with three main research goals: (1) embodied perception, using data captured by visual sensors to perform point cloud extraction or 3D reconstruction, combined with pre-trained models, to understand the target object and external environment and directly predict the execution of actions; (2) embodied strategy: In imitation learning, the pre-trained model is used to enhance data or as a feature extractor to enhance the generalization ability of the model. In reinforcement learning, the pre-trained model is used to obtain the optimal reward function, which improves the learning efficiency and ability of reinforcement learning; (3) embodied agent: The pre-trained model adopts hierarchical or holistic execution to achieve end-to-end robot control. Finally, the challenges of the current research are summarized, and a perspective on feasible technical routes is provided.

Robotic manipulation policies have made significant progress in recent years, yet their target-cognitive generalization capability remains insufficient when facing unseen targets and scenarios with similar distractors. Existing methods mostly rely on implicit alignment between language descriptions and global visual features. When target appearance or geometric shape changes, or when similar distractors are present, they struggle to stably establish the correspondence between the language-specified target and action generation, thereby affecting manipulation success rates. To address this problem, this paper proposes TCG-BP (Target-Cognitive Generalization Bimanual Policy), a target-prior-driven bimanual manipulation policy. The method converts language target descriptions into temporally consistent pixel-level target masks, and enhances visual representations through image–mask collaborative encoding and fusion. In the action generation stage, the global scene representation and target-focused representation are extracted from the enhanced visual representations and injected into the policy network in a differentiated manner, enabling continuous action prediction to be constrained by scene context while being guided by target priors. On the RoboTwin 2.0 benchmark, TCG-BP improves the average success rate over π0 by 10.2, 12.2, and 13.8 percentage points under the Seen, Unseen Object, and Unseen Distractor settings, respectively. Experimental results verify the effectiveness of the proposed method.