Tencent Releases Embodied World Cognition Foundation Model RxBrain, Unifying Reasoning and Visual Imagination

On July 15, Tencent Robotics X Lab, in collaboration with Tencent Hunyuan, released and open-sourced two embodied intelligence foundation models: Hy-Embodied-VLM-1.0 and Hy-Embodied-RxBrain-1.0. The former, based on the Hunyuan A3B architecture, achieves the performance of the previous flagship model with 1/10 the computation; the latter unifies understanding and generation of text, images, and video in a single model, enabling synergy between language reasoning and visual target imagination.

Model Architecture and Capabilities

• Hy-Embodied-VLM-1.0: A second-generation embodied VLM foundation model, building capabilities across three levels: physical space state understanding, action-change understanding, and temporal and adaptive reasoning, enhancing scene perception, action analysis and planning, and navigation. Based on Hunyuan A3B, it achieves a comprehensive score of 65.6 on 37 evaluation tasks, approaching the previous flagship A32B model and significantly outperforming competitors of similar scale.
• Hy-Embodied-RxBrain-1.0: An embodied world cognition foundation model, adopting a modality-routed Mixture-of-Transformers architecture that aligns language reasoning and visual target imagination around the same task. Language handles task decomposition, action logic, and constraints; visual target images describe the intermediate and final states to be achieved at each step, jointly providing more complete conditions for downstream action models.

Training Data and Evaluation

• Training Data: RxBrain is trained on over 50,000 hours of high-quality embodied data, including first-person view data, real robot data, and simulation data. After quality filtering, approximately 210 million training samples are constructed, covering four granularities (continuous action state imagination, atomic action planning, high-level subtask planning, and final target state imagination).
• Evaluation Benchmark: The team built RxBrain-Bench, comprising three progressive tasks: embodied VQA, world state prediction, and joint subgoal planning. On the joint planning task, RxBrain achieves a comprehensive planning score of 0.68, outperforming modular solutions (e.g., Cosmos3-Nano's 0.521). The average success rate on three real-robot manipulation tasks is 87%, surpassing π0 and π0.5.

Open Source and Impact

Both models are open-sourced, and developers can download and deploy them on GitHub and Hugging Face. This release provides new foundation model support for the embodied intelligence field, potentially driving the paradigm shift from "perception-action" to "cognition-reasoning-planning" in robotics.