Vesta: A Generalist Embodied Reasoning Model
Summary
Vesta is a unified embodied generalist model that integrates localization, spatial reasoning, navigation, and long-horizon planning into a single foundation model, outperforming specialized models by over 20% on benchmarks and over 35% on real-world robotic tasks.
View Cached Full Text
Cached at: 06/30/26, 03:34 AM
Paper page - Vesta: A Generalist Embodied Reasoning Model
Source: https://huggingface.co/papers/2606.20905 Authors:
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
Abstract
Vesta is a unified embodied generalist model that integrates localization, spatial reasoning, navigation, and long-horizon planning into a single foundation model, outperforming specialized models in both benchmark tests and real-world robotic applications.
Robots operating in open-world environments must seamlessly integratelocalization,spatial reasoning, navigation, andlong-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 unifiedembodied generalistthat consolidates these capabilities into a singlefoundation model. Our approach combines a diverse and massive curated corpus designed to inducespatial groundingand a simplemultimodal memoryharness 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.
View arXiv pageView PDFAdd to collection
Get this paper in your agent:
hf papers read 2606\.20905
Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash
Models citing this paper0
No model linking this paper
Cite arxiv.org/abs/2606.20905 in a model README.md to link it from this page.
Datasets citing this paper0
No dataset linking this paper
Cite arxiv.org/abs/2606.20905 in a dataset README.md to link it from this page.
Spaces citing this paper0
No Space linking this paper
Cite arxiv.org/abs/2606.20905 in a Space README.md to link it from this page.
Collections including this paper0
No Collection including this paper
Add this paper to acollectionto link it from this page.
Similar Articles
VESTA: Visual Exploration with Statistical Tool Agents
This paper introduces VESTA, a framework that equips vision-language models with dynamically growing toolkits for data exploration and statistical model refinement, outperforming prior agent-based methods on complex scientific modeling tasks. The authors also present Dawn, a benchmark for distribution fitting and time series modeling, including real-world astronomy challenges.
World Value Models for Robotic Manipulation
The paper presents World Value Model (WVM), a generalist robotic value model that combines world models with value estimation to accurately assess task progression and improve robotic policy learning from mixed-quality data, achieving state-of-the-art results on standard benchmarks and a new suboptimal data benchmark.
GeneralVLA-2: Geometry-Aware Reconstruction and Governed Memory for Robot Planning
GeneralVLA-2 introduces GeoFuse-MV3D for improved 3D reconstruction and a governed KnowledgeBank for better memory management in robotic manipulation tasks, achieving performance gains on several benchmarks.
More Thought, Less Accuracy? On the Dual Nature of Reasoning in Vision-Language Models
This paper uncovers that prolonged reasoning in vision-language models can impair perceptual grounding, causing recognition failures on basic visual questions. It proposes Vision-Anchored Policy Optimization (VAPO) to steer reasoning toward visually grounded trajectories, achieving state-of-the-art performance with the VAPO-Thinker-7B model.
Bridging Physical Reasoning and Task Generalization via Visual Action Outcome Reasoning Alignment
VAORA proposes a novel reward design for vision-language models that aligns reasoning with visual contexts and action outcomes, improving physical reasoning and generalization across unseen tasks and environments.