XEmbodied: A Foundation Model with Enhanced Geometric and Physical Cues for Large-Scale Embodied Environments

With the rapid advancement of artificial intelligence technologies, vision-language models (VLMs) have been widely applied in the field of multimodal learning. However, existing VLMs are mostly based on 2D image-text pretraining, lacking an understanding of 3D geometric information and physical cues, which limits their application in complex environments. In recent years, researchers have begun to focus on how to integrate geometric and physical information into VLMs to enhance their performance in large-scale embodied environments.

Current vision-language models exhibit significant deficiencies in geometric reasoning and domain semantics understanding, especially in large-scale embodied environments. The core issue is that existing models are primarily based on 2D image-text pretraining, lacking the capability to integrate 3D geometric information and physical cues. This not only limits the models' application in complex environments but also hinders further development in the field of multimodal learning.

The core innovation of the XEmbodied model lies in its integration of 3D geometric information and physical cues into vision-language models through a structured 3D Adapter and an Efficient Image-Embodied Adapter. Specifically, the 3D Adapter is used to integrate geometric representations into the model, enhancing its spatial reasoning capabilities, while the Efficient Image-Embodied Adapter distills physical signals into context tokens, improving the model's understanding of physical cues. This innovation not only enhances the model's capabilities in geometric reasoning and domain semantics understanding but also provides new research directions for the field of multimodal learning.

• �� Structured 3D Adapter: Integrates 3D geometric information into vision-language models, enhancing spatial reasoning capabilities.
• �� Efficient Image-Embodied Adapter: Distills physical signals into context tokens, improving understanding of physical cues.
• �� Progressive Domain Curriculum: Gradually introduces different domain curricula to enhance model generalization capabilities.
• �� Reinforcement Learning Post-Training: Further optimizes model performance through reinforcement learning, ensuring adaptability in complex environments.

The experimental design includes validating the performance of the XEmbodied model across multiple public benchmarks. Datasets used include CLEVRER and Waymo Open Dataset, with baseline models being existing state-of-the-art vision-language models. Metrics include spatial reasoning accuracy and traffic semantics analysis accuracy, with key hyperparameters including the structure of the 3D Adapter and the configuration of the Efficient Image-Embodied Adapter. Ablation studies are conducted to verify the contribution of each component to model performance.

Experimental results show that the XEmbodied model performs excellently across multiple benchmarks. It improved performance by 15% on the CLEVRER dataset for spatial reasoning tasks and achieved a 12% accuracy increase on the Waymo Open Dataset for traffic semantics analysis. Ablation studies revealed a 20% performance drop when the 3D Adapter was removed, highlighting the critical role of geometric information integration.

The XEmbodied model has broad application potential in fields such as autonomous driving and robotic navigation. Its enhanced capabilities in geometric reasoning and physical cues understanding enable it to perform excellently in complex traffic scenarios and dynamic environments. This not only provides technical support for related industries but also offers new directions for research in the field of multimodal learning.

While XEmbodied performs well across multiple benchmarks, it faces challenges in rapidly changing dynamic environments. Additionally, the model's high computational resource requirements during training may limit its application in resource-constrained settings. Future research directions include optimizing the model's computational efficiency for deployment in resource-constrained environments, exploring more domain curricula to further enhance generalization capabilities, and improving adaptability to rapid changes in dynamic environments.