研究人员推出Hy-Embodied-RxBrain，这是一种新颖的具身认知基础模型，它整合了语言和视觉推理与想象力。与专注于场景理解或视觉预测的现有模型不同，RxBrain将语言中的抽象规划结构与世界状态的具身视觉想象相结合。该模型采用了Transformer混合架构，并在具身理解、生成和连续机器人动作方面展示了能力，为实际应用带来了希望。 AI
影响 该模型通过整合语言和视觉推理，推动了具身认知的发展，有望实现能够在物理环境中进行复杂任务规划和执行的更先进的AI代理。
排序理由 该集群包含一篇详细介绍新模型和基准的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2607.14187v1 Announce Type: new Abstract: Embodied cognition requires agents to connect high-level task reasoning with the physical states to be achieved. We introduce Hy-Embodied-RxBrain, an embodied cognition foundation model with joint language-visual reasoning and imagi…
Embodied cognition requires agents to connect high-level task reasoning with the physical states to be achieved. We introduce Hy-Embodied-RxBrain, an embodied cognition foundation model with joint language-visual reasoning and imagination. Unlike vision-language models that empha…