Vesta: A Generalist Embodied Reasoning Model
Authors: name: Johan Bjorck, name: Zhiqi Li, name: Yunze Man, name: Jing Wang, name: An-Chieh Cheng, name: Sifei Liu, name: Shihao Wang, name: Zhiding Yu, name: Abhishek Badki, name: Stan Birchfield, name: Valts Blukis, name: Yevgen Chebotar, name: Siyi Chen, name: Sicong Leng, name: Yu-Cheng Chou, name: Tianli Ding, name: Boyi Li, name: Zhengyi Luo, name: Hang Su, name: Jonathan Tremblay, name: Tingwu Wang, name: Bowen Wen, name: Jimmy Wu, name: Xianghui Xie, name: Hanrong Ye, name: Hongxu Yin, name: K. R. Zentner, name: Liangyan Gui, name: Yu-Xiong Wang, name: Yuke Zhu, name: Linxi "Jim" Fan, name: Jan Kautz
Organization: NVIDIA

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.