Vesta：通才型具身推理模型

摘要

在开放世界环境中运行的机器人必须无缝整合定位、空间推理、导航与长时域规划能力。尽管专用模型在单项任务上表现优异，但部署多模型堆叠方案不仅计算成本高昂，还容易引发级联错误。我们提出Vesta——一种将上述能力统一整合至单一基础模型的具身通才系统。该方法结合大规模多样化精选语料库（旨在诱导空间感知能力）与简洁的多模态记忆框架，从而实现超长时域推理。在多种基准测试中，Vesta较单项最优基线模型平均提升超过20%，较类别最优基线模型集成方案平均提升超过10%——这证明了通用模型能够媲美甚至超越专用模型。在需要记忆与推理能力的真实机器人任务中，Vesta将任务成功率提升逾35%。本研究由此表明：相较于组合专用模型，单一通用模型是一种可行、可扩展且更具优势的替代方案。

English

Robots operating in open-world environments must seamlessly integrate localization, spatial reasoning, navigation, and long-horizon planning. While specialist models excel at individual tasks, deploying a multi-model stack is computationally expensive and prone to cascading errors. We present Vesta, a unified embodied generalist that consolidates these capabilities into a single foundation model. Our approach combines a diverse and massive curated corpus designed to induce spatial grounding and a simple multimodal memory harness that enables reasoning over extended time horizons. Across diverse benchmarks, Vesta on average beats individual SOTA baselines by >20% and beats an ensemble of per-category-best baselines by >10% -- thus demonstrating that a generalist model can match or exceed specialists. On real-world robotic tasks requiring memory and reasoning, Vesta improves task success by >35\%. Our work thus demonstrates that a single generalist is a feasible, scalable, and arguably preferable alternative to combining specialists.