Vesta：一個通才型具身推理模型

摘要

在開放世界環境中運作的機器人必須無縫整合定位、空間推理、導航與長程規劃。儘管專精模型在個別任務上表現優異，但部署多模型堆疊不僅計算成本高昂，且容易產生連鎖誤差。我們提出 Vesta，這是一個統一的具身通才模型，將上述能力整合至單一基礎模型中。我們的方法結合了精心設計的大規模多樣化語料庫（旨在誘發空間基礎化）以及一個簡潔的多模態記憶機制，使其能夠在較長的時間跨度內進行推理。在各種基準測試中，Vesta 平均勝過個別 SOTA 基準線超過 20%，並勝過各類別最佳基準線之集成超過 10%——從而證明通用模型能夠匹配甚至超越專精模型。在需要記憶與推理的真實世界機器人任務中，Vesta 將任務成功率提升了超過 35%。因此，我們的研究證明，單一通用模型是結合專精模型的一種可行、可擴展且可論證更佳之替代方案。

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.