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arxiv: 2606.20905 · v1 · pith:SAKS4ODJnew · submitted 2026-06-18 · 💻 cs.RO · cs.AI

Vesta: A Generalist Embodied Reasoning Model

Johan Bjorck , Zhiqi Li , Yunze Man , Jing Wang , An-Chieh Cheng , Sifei Liu , Shihao Wang , Zhiding Yu
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Abhishek Badki Stan Birchfield Valts Blukis Yevgen Chebotar Siyi Chen Sicong Leng Yu-Cheng Chou Tianli Ding Boyi Li Zhengyi Luo Hang Su Jonathan Tremblay Tingwu Wang Bowen Wen Jimmy Wu Xianghui Xie Hanrong Ye Hongxu Yin K.R. Zentner Liangyan Gui Yu-Xiong Wang Yuke Zhu Linxi "Jim" Fan Jan Kautz
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Pith reviewed 2026-06-26 16:53 UTC · model grok-4.3

classification 💻 cs.RO cs.AI
keywords embodied AIgeneralist modelrobotic reasoningspatial groundingmultimodal memoryfoundation modellong-horizon planningunified model
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The pith

A single generalist model for embodied reasoning outperforms both individual specialists and their ensembles by over 20 percent on average.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Vesta as a single foundation model that integrates localization, spatial reasoning, navigation, and long-horizon planning for robots in open-world settings. Instead of deploying separate specialist models that incur high compute costs and risk cascading errors, Vesta trains on a large curated corpus to build spatial grounding and adds a simple multimodal memory harness to handle extended sequences. Across benchmarks this unified approach exceeds individual state-of-the-art baselines by more than 20 percent and an ensemble of the best per-category models by more than 10 percent. On physical robotic tasks that demand memory and reasoning, task success rises by more than 35 percent, indicating that one model can replace modular stacks while remaining scalable.

Core claim

Vesta consolidates localization, spatial reasoning, navigation, and long-horizon planning into a single foundation model. The model is trained on a diverse and massive curated corpus designed to induce spatial grounding together with a simple multimodal memory harness that supports reasoning over extended time horizons. On diverse benchmarks the resulting generalist exceeds individual SOTA baselines by more than 20 percent on average and an ensemble of per-category-best baselines by more than 10 percent; on real-world robotic tasks requiring memory and reasoning it raises task success by more than 35 percent.

What carries the argument

Vesta, the unified embodied generalist model that combines a curated corpus for spatial grounding with a multimodal memory harness for long-horizon reasoning inside one foundation model.

If this is right

  • A generalist model can match or exceed specialists across embodied tasks without requiring per-task model selection.
  • Replacing a multi-model stack with one foundation model reduces both computational expense and the risk of cascading errors.
  • The combination of curated corpus and memory harness allows a single model to maintain performance over extended time horizons.
  • Real-world robotic task success improves by more than 35 percent when memory and reasoning are handled inside the same model.
  • Generalist approaches become a feasible and scalable alternative to specialist combinations in open-world robotics.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Deployment pipelines for robots could simplify dramatically if one model reliably replaces several specialists.
  • Data curation strategies that emphasize spatial grounding may transfer to other perception-planning domains.
  • Extending the memory harness length or corpus diversity offers a direct route to longer autonomous sequences.
  • Open-world benchmarks that integrate all four capabilities at once would provide the clearest test of whether the generalist advantage holds.

Load-bearing premise

A diverse and massive curated corpus can be designed to induce spatial grounding while a simple multimodal memory harness enables reasoning over extended time horizons inside a single model without the cascading errors typical of multi-model stacks.

What would settle it

A controlled test on a new benchmark that requires simultaneous localization, spatial reasoning, and multi-step planning in which Vesta achieves lower success rates than the ensemble of per-category specialists or introduces error rates comparable to separate-model pipelines.

Figures

Figures reproduced from arXiv: 2606.20905 by Abhishek Badki, An-Chieh Cheng, Bowen Wen, Boyi Li, Hang Su, Hanrong Ye, Hongxu Yin, Jan Kautz, Jimmy Wu, Jing Wang, Johan Bjorck, Jonathan Tremblay, K.R. Zentner, Liangyan Gui, Linxi "Jim" Fan, Shihao Wang, Sicong Leng, Sifei Liu, Siyi Chen, Stan Birchfield, Tianli Ding, Tingwu Wang, Valts Blukis, Xianghui Xie, Yevgen Chebotar, Yu-Cheng Chou, Yuke Zhu, Yunze Man, Yu-Xiong Wang, Zhengyi Luo, Zhiding Yu, Zhiqi Li.

Figure 1
Figure 1. Figure 1: Vesta unifies localization, navigation, embodied reasoning, and action planning into a single generalist [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Vesta is a generalist embodied model, supporting multimodal inputs and hierarchal control. The four [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Demonstration of the action planning task. The model is tasked to plan the next subtask based on the overall objective and the memory context. Intermediate steps are omitted. 2. Methods Vesta is finetuned from the Qwen3-VL-8B [116] base model. Our supervised fine-tuning (SFT) strategy builds base capabilities in localization, navigation, embodied reasoning, and memory-conditioned planning. Significant effo… view at source ↗
Figure 4
Figure 4. Figure 4: SFT data mixture. Our SFT mix spans six categories. 4 [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Demonstration of navigation evaluation. Model outputs turns, forward￾to-locations, and stop actions. Intermediate steps are omitted. 4.3. Navigation We evaluate navigation capabilities using the NavSuite benchmark. It runs the R2R val_unseen split (1839 episodes in held-out Matterport3D scenes) inside the same Habitat simulator. Note that all val-unseen scenes and episodes are excluded from our SFT data. A… view at source ↗
Figure 6
Figure 6. Figure 6: Real robot tasks. We evaluate Vesta as a planner model on real bimanual robots with three reasoning and memory-heavy tasks: find object, count fruits, and memorize candy. Memorize Candy 0% 25% 50% 75% 100% Success Rate (%) Find Object Count Fruits Average Actor Only Actor + Planner (Qwen3-VL) Actor + Planner (Ours) [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7 [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Hierarchical execution. The planner VLM takes as input a task specified in natural language, and continuously observes the images from the robot. It stores both text and image in a memory. At any given time, the planner sends the next subtask to the actor VLA. The actor consumes images, states and subtasks to produce robot actions. In this work, we focus on improving the planner. Task # Train # Eval Succes… view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper presents Vesta, a unified embodied generalist foundation model that integrates localization, spatial reasoning, navigation, and long-horizon planning. It relies on a diverse curated corpus to induce spatial grounding and a multimodal memory harness for reasoning over extended horizons. The central claims are that Vesta outperforms individual SOTA baselines by >20% on average and an ensemble of per-category-best baselines by >10% across benchmarks, while improving real-world robotic task success by >35%, demonstrating that a single generalist model is a feasible and preferable alternative to specialist stacks.

Significance. If the performance claims hold under rigorous evaluation, the result would be significant for embodied AI and robotics. It would provide evidence that generalist models can match or exceed specialist performance without the computational cost or cascading errors of multi-model systems, potentially shifting the field toward unified architectures for open-world tasks.

major comments (1)
  1. The abstract states the key quantitative claims (>20% average improvement over individual SOTA baselines, >10% over per-category ensembles, and >35% on real-world tasks) but supplies no methods details, dataset descriptions, statistical tests, or error analysis. This absence is load-bearing for the central claim that a generalist model can match or exceed specialists, as the evaluation protocol cannot be assessed for validity or reproducibility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their thoughtful review. The single major comment raises a valid point about the abstract's level of detail. We address it directly below.

read point-by-point responses

  1. Referee: The abstract states the key quantitative claims (>20% average improvement over individual SOTA baselines, >10% over per-category ensembles, and >35% on real-world tasks) but supplies no methods details, dataset descriptions, statistical tests, or error analysis. This absence is load-bearing for the central claim that a generalist model can match or exceed specialists, as the evaluation protocol cannot be assessed for validity or reproducibility.

    Authors: We agree that the abstract, by design, is a concise summary and does not contain the full methods, dataset descriptions, statistical tests, or error analysis. These elements are provided in the full manuscript: Section 3 details the model architecture and training corpus; Section 4 describes the benchmarks, baselines, and evaluation protocol including statistical significance testing; and Section 5 presents error analysis and real-world deployment results. The abstract's quantitative claims are therefore grounded in the reported experiments. If the referee believes a brief mention of the evaluation setup would strengthen the abstract, we are happy to add one sentence summarizing the protocol and datasets used. revision: partial

Circularity Check

0 steps flagged

No derivation chain present; empirical claims only

full rationale

The provided abstract and manuscript description contain no equations, derivations, self-citations, or load-bearing mathematical steps. All claims are empirical performance comparisons on benchmarks and real-world tasks. No self-definitional reductions, fitted inputs called predictions, or ansatz smuggling via citation are identifiable. The result is self-contained as an empirical demonstration against external baselines.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no free parameters, axioms, or invented entities can be identified.

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discussion (0)

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Reference graph

Works this paper leans on

163 extracted references · 3 canonical work pages

  1. [1]

    Edgenavmamba: Mamba optimized object detection for energy efficient edge devices.arXiv preprint arXiv:2510.14946, 2025

    Romina Aalishah, Mozhgan Navardi, and Tinoosh Mohsenin. Edgenavmamba: Mamba optimized object detection for energy efficient edge devices.arXiv preprint arXiv:2510.14946, 2025. 24

    arXiv 2025

  2. [2]

    Scaling spatial intelligence with multimodal foundation models

    Zhongang Cai, Ruisi Wang, Chenyang Gu, Fanyi Pu, Junxiang Xu, Yubo Wang, Wanqi Yin, Zhitao Yang, Chen Wei, Qingping Sun, Tongxi Zhou, Jiaqi Li, Hui En Pang, Oscar Qian, Yukun Wei, Zhiqian Lin, Xuanke Shi, Kewang Deng, Xiaoyang Han, Zukai Chen, Xiangyu Fan, Hanming Deng, Lewei Lu, Liang Pan, Bo Li, Ziwei Liu, Quan Wang, Dahua Lin, and Lei Yang. Scaling spa...

    2026

  3. [3]

    Jiahang Cao, Yize Huang, Hanzhong Guo, Rui Zhang, Mu Nan, Weijian Mai, Jiaxu Wang, Hao Cheng, Jingkai Sun, Gang Han, Wen Zhao, Qiang Zhang, Yijie Guo, Qihao Zheng, Chunfeng Song, Xiao Li, Ping Luo, and Andrew F. Luo. Compose your policies! improving diffusion-based or flow-based robot policies via test-time distribution-level composition. InICLR, 2026. 1, 9, 24

    2026

  4. [4]

    Rethinking backbone design for lightweight 3d object detection in lidar

    Adwait Chandorkar, Hasan Tercan, and Tobias Meisen. Rethinking backbone design for lightweight 3d object detection in lidar. InICCV, 2025. 24

    2025

  5. [5]

    Diwa: Diffusion policy adaptation with world models.CoRL, 2025

    Akshay L Chandra, Iman Nematollahi, Chenguang Huang, Tim Welschehold, Wolfram Burgard, and Abhinav Valada. Diwa: Diffusion policy adaptation with world models.CoRL, 2025. 24

    2025

  6. [6]

    Matterport3d: Learning from rgb-d data in indoor environments

    Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Niessner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang. Matterport3d: Learning from rgb-d data in indoor environments. arXiv preprint arXiv:1709.06158, 2017. 3

    Pith/arXiv arXiv 2017

  7. [7]

    Commonsense reasoning for legged robot adaptation with vision-language models.arXiv preprint arXiv:2407.02666, 2024

    Annie S Chen, Alec M Lessing, Andy Tang, Govind Chada, Laura Smith, Sergey Levine, and Chelsea Finn. Commonsense reasoning for legged robot adaptation with vision-language models.arXiv preprint arXiv:2407.02666, 2024. 10, 25 10 Vesta: A Generalist Embodied Reasoning Model

    arXiv 2024

  8. [8]

    Fast-in-slow: A dual-system foundation model unifying fast manipulation within slow reasoning.arXiv preprint arXiv:2506.01953, 2025

    Hao Chen, Jiaming Liu, Chenyang Gu, Zhuoyang Liu, Renrui Zhang, Xiaoqi Li, Xiao He, Yandong Guo, Chi-Wing Fu, Shanghang Zhang, and Pheng-Ann Heng. Fast-in-slow: A dual-system foundation model unifying fast manipulation within slow reasoning.arXiv preprint arXiv:2506.01953, 2025. 24

    arXiv 2025

  9. [9]

    History-aware visuomotor policy learning via point tracking

    Jingjing Chen, Hongjie Fang, Chenxi Wang, Shiquan Wang, and Cewu Lu. History-aware visuomotor policy learning via point tracking. InICRA, 2026. 10, 25

    2026

  10. [10]

    Robo2vlm: Vi- sual question answering from large-scale in-the-wild robot manipulation datasets.arXiv preprint arXiv:2505.15517, 2025

    Kaiyuan Chen, Shuangyu Xie, Zehan Ma, Pannag R Sanketi, and Ken Goldberg. Robo2vlm: Vi- sual question answering from large-scale in-the-wild robot manipulation datasets.arXiv preprint arXiv:2505.15517, 2025. 5

    arXiv 2025

  11. [11]

    Rlrc: Reinforcement learning-based recovery for compressed vision-language- action models.arXiv preprint arXiv:2506.17639, 2025

    Yuxuan Chen and Xiao Li. Rlrc: Reinforcement learning-based recovery for compressed vision-language- action models.arXiv preprint arXiv:2506.17639, 2025. 24

    Pith/arXiv arXiv 2025

  12. [12]

    Navila: Legged robot vision-language-action model for navigation.arXiv preprint arXiv:2412.04453, 2024

    An-Chieh Cheng, Yandong Ji, Zhaojing Yang, Zaitian Gongye, Xueyan Zou, Jan Kautz, Erdem Bıyık, Hongxu Yin, Sifei Liu, and Xiaolong Wang. Navila: Legged robot vision-language-action model for navigation.arXiv preprint arXiv:2412.04453, 2024. 1, 2, 10

    arXiv 2024

  13. [13]

    Spatialrgpt: Grounded spatial reasoning in vision-language models

    An-Chieh Cheng, Hongxu Yin, Yang Fu, Qiushan Guo, Ruihan Yang, Jan Kautz, Xiaolong Wang, and Sifei Liu. Spatialrgpt: Grounded spatial reasoning in vision-language models. InNeurIPS, 2024. 24

    2024

  14. [14]

    Pointarena: Probing multimodal grounding through language- guided pointing.arXiv preprint arXiv:2505.09990, 2025

    Long Cheng, Jiafei Duan, Yi Ru Wang, Haoquan Fang, Boyang Li, Yushan Huang, Elvis Wang, Ainaz Eftekhar, Jason Lee, Wentao Yuan, et al. Pointarena: Probing multimodal grounding through language- guided pointing.arXiv preprint arXiv:2505.09990, 2025. 5

    arXiv 2025

  15. [15]

    EgoThink: Evaluating first-person perspective thinking capability of vision-language models

    Sijie Cheng, Zhicheng Guo, Jingwen Wu, Kechen Fang, Peng Li, Huaping Liu, and Yang Liu. EgoThink: Evaluating first-person perspective thinking capability of vision-language models. InCVPR, 2024. 25

    2024

  16. [16]

    Mobility VLA: Multimodal instruction navigation with long-context VLMs and topological graphs.arXiv preprint arXiv:2407.07775, 2024

    Hao-Tien Lewis Chiang, Zhuo Xu, Zipeng Fu, Mithun George Jacob, Tingnan Zhang, Tsang-Wei Edward Lee, Wenhao Yu, Connor Schenck, David Rendleman, Dhruv Shah, Fei Xia, Jasmine Hsu, Jonathan Hoech, Pete Florence, Sean Kirmani, Sumeet Singh, Vikas Sindhwani, Carolina Parada, Chelsea Finn, Peng Xu, Sergey Levine, and Jie Tan. Mobility VLA: Multimodal instructi...

    arXiv 2024

  17. [17]

    Rethinking progression of memory state in robotic manipulation: An object-centric perspective

    Nhat Chung, Taisei Hanyu, Toan Nguyen, Huy Le, Frederick Bumgarner, Duy Minh Ho Nguyen, Khoa Vo, Kashu Yamazaki, Chase Rainwater, Tung Kieu, Anh Nguyen, and Ngan Le. Rethinking progression of memory state in robotic manipulation: An object-centric perspective. InAAAI, 2026. 10, 25

    2026

  18. [18]

    Open x-embodiment: Robotic learning datasets and rt-x models

    Open X-Embodiment Collaboration. Open x-embodiment: Robotic learning datasets and rt-x models. In ICLR, 2024. 10, 25

    2024

  19. [19]

    Rynnbrain: Open embodied foundation models.arXiv preprint arXiv:2602.14979, 2026

    Ronghao Dang, Jiayan Guo, Bohan Hou, Sicong Leng, Kehan Li, Xin Li, Jiangpin Liu, Yunxuan Mao, Zhikai Wang, Yuqian Yuan, et al. Rynnbrain: Open embodied foundation models.arXiv preprint arXiv:2602.14979, 2026. 2, 4, 6, 7, 9, 24

    arXiv 2026

  20. [20]

    Monoslam: Real-time single camera slam.IEEE transactions on pattern analysis and machine intelligence, 29(6):1052–1067, 2007

    Andrew J Davison, Ian D Reid, Nicholas D Molton, and Olivier Stasse. Monoslam: Real-time single camera slam.IEEE transactions on pattern analysis and machine intelligence, 29(6):1052–1067, 2007. 10

    2007

  21. [21]

    Q-transformer: Scalable offline reinforcement learning via autoregressive q-functions

    Google DeepMind. Q-transformer: Scalable offline reinforcement learning via autoregressive q-functions. arXiv preprint arXiv:2309.10150, 2023. 24

    arXiv 2023

  22. [22]

    Rt-2: Vision-language-action models transfer web knowledge to robotic control

    Google DeepMind. Rt-2: Vision-language-action models transfer web knowledge to robotic control. In CoRL, 2023. 10, 24, 25

    2023

  23. [23]

    QUAR-VLA: Vision-language-action model for quadruped robots

    Pengxiang Ding, Han Zhao, Wenjie Zhang, Wenxuan Song, Min Zhang, Siteng Huang, Ningxi Yang, and Donglin Wang. QUAR-VLA: Vision-language-action model for quadruped robots. InECCV, 2024. 24 11 Vesta: A Generalist Embodied Reasoning Model

    2024

  24. [24]

    Scaling cross-embodied learning: One policy for manipulation, navigation, locomotion and aviation

    Ria Doshi, Homer Walke, Oier Mees, Sudeep Dasari, and Sergey Levine. Scaling cross-embodied learning: One policy for manipulation, navigation, locomotion and aviation. InCoRL, 2024. 10, 25

    2024

  25. [25]

    An image is worth 16x16 words: Transformers for image recognition at scale

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. InICLR,

  26. [27]

    Palm-e: An embodied multimodal language model.arXiv preprint arXiv:2303.03378, 2023

    Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, et al. Palm-e: An embodied multimodal language model.arXiv preprint arXiv:2303.03378, 2023. 9

    Pith/arXiv arXiv 2023

  27. [28]

    EmbSpatial-bench: Benchmarking spatial understanding for embodied tasks with large vision-language models

    MengfeiDu, BinhaoWu, ZejunLi, XuanjingHuang, andZhongyuWei. EmbSpatial-bench: Benchmarking spatial understanding for embodied tasks with large vision-language models. InACL, 2024. 5, 25

    2024

  28. [29]

    Fast ECoT: Efficient embodied chain-of-thought via thoughts reuse.arXiv preprint arXiv:2506.07639, 2025

    Zhekai Duan, Yuan Zhang, Shikai Geng, Gaowen Liu, Joschka Boedecker, and Chris Xiaoxuan Lu. Fast ECoT: Efficient embodied chain-of-thought via thoughts reuse.arXiv preprint arXiv:2506.07639, 2025. 24

    arXiv 2025

  29. [30]

    CNS-bench: Benchmarking image classifier robustness under continuous nuisance shifts

    Olaf Dünkel, Artur Jesslen, Jiahao Xie, Christian Theobalt, Christian Rupprecht, and Adam Kortylewski. CNS-bench: Benchmarking image classifier robustness under continuous nuisance shifts. InICCV, 2025. 25

    2025

  30. [31]

    Sam2act: Integrating visual foundation model with a memory architecture for robotic manipulation

    Haoquan Fang, Markus Grotz, Wilbert Pumacay, Yi Ru Wang, Dieter Fox, Ranjay Krishna, and Jiafei Duan. Sam2act: Integrating visual foundation model with a memory architecture for robotic manipulation. CVPR, 2025. 24

    2025

  31. [32]

    Helix: A vision-language-action model for generalist humanoid control.arXiv preprint,

    Figure AI Team. Helix: A vision-language-action model for generalist humanoid control.arXiv preprint,

  32. [33]

    Smith, Wei-Chiu Ma, and Ranjay Krishna

    Xingyu Fu, Yushi Hu, Bangzheng Li, Yu Feng, Haoyu Wang, Xudong Lin, Dan Roth, Noah A. Smith, Wei-Chiu Ma, and Ranjay Krishna. BLINK: Multimodal large language models can see but not perceive. InECCV, 2024. 25

    2024

  33. [34]

    Self-improving embodied foundation models.NeurIPS, 2025

    Seyed Kamyar Seyed Ghasemipour, Ayzaan Wahid, Jonathan Tompson, Pannag R Sanketi, and Igor Mordatch. Self-improving embodied foundation models.NeurIPS, 2025. 24

    2025

  34. [35]

    Ego3d-bench: Egocentric 3d perception for wearable ai.arXiv preprint arXiv:2509.06266, 2025

    MohsenGholami, AhmadRezaei, ZhouWeimin, SitongMao, ShunboZhou, YongZhang, andMohammad Akbari. Ego3d-bench: Egocentric 3d perception for wearable ai.arXiv preprint arXiv:2509.06266, 2025. 25

    arXiv 2025

  35. [36]

    Amego: Active memory from long egocentric videos

    Gabriele Goletto, Tushar Nagarajan, Giuseppe Averta, and Dima Damen. Amego: Active memory from long egocentric videos. InECCV, pages 92–110. Springer, 2024. 10, 25

    2024

  36. [37]

    ACE-Brain-0: Spatial intelligence as a shared scaffold for universal embodiments.arXiv preprint arXiv:2603.03198, 2026

    Ziyang Gong, Zehang Luo, Anke Tang, Zhe Liu, Shi Fu, Zhi Hou, Ganlin Yang, Weiyun Wang, Xiaofeng Wang, Jianbo Liu, Gen Luo, Haolan Kang, Shuang Luo, Yue Zhou, Yong Luo, Li Shen, Xiaosong Jia, Yao Mu, Xue Yang, Chunxiao Liu, Junchi Yan, Hengshuang Zhao, Dacheng Tao, and Xiaogang Wang. ACE-Brain-0: Spatial intelligence as a shared scaffold for universal emb...

    arXiv 2026

  37. [38]

    Deepseek-r1 incentivizes reasoning in llms through reinforcement learning

    Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, Runxin Xu, Ruoyu Zhang, Shirong Ma, Xiao Bi, et al. Deepseek-r1 incentivizes reasoning in llms through reinforcement learning. Nature, 645(8081):633–638, 2025. 9

    2025

  38. [39]

    LVIS: A dataset for large vocabulary instance segmentation

    Agrim Gupta, Piotr Dollár, and Ross Girshick. LVIS: A dataset for large vocabulary instance segmentation. InCVPR, pages 5356–5364, 2019. doi: 10.1109/CVPR.2019.00550. 3

    work page doi:10.1109/cvpr.2019.00550 2019

  39. [40]

    SPARE3D: A dataset for SPAtial REasoning on Three-View line drawings

    Wenyu Han, Siyuan Xiang, Chenhui Liu, Ruoyu Wang, and Chen Feng. SPARE3D: A dataset for SPAtial REasoning on Three-View line drawings. InCVPR, 2020. 25

    2020

  40. [41]

    Mimo-embodied: X-embodied foundation model technical report, 2026

    Xiaoshuai Hao, Lei Zhou, Zhijian Huang, Zhiwen Hou, Yingbo Tang, Lingfeng Zhang, Guang Li, Zheng Lu, Shuhuai Ren, Xianhui Meng, Yuchen Zhang, Jing Wu, Jinghui Lu, Chenxu Dang, Jiayi Guan, Jianhua Wu, Zhiyi Hou, Hanbing Li, Shumeng Xia, Mingliang Zhou, Yinan Zheng, Zihao Yue, Shuhao Gu, Hao Tian, Yuannan Shen, Jianwei Cui, Wen Zhang, Shaoqing Xu, Bing Wang...

    Pith/arXiv arXiv 2026

  41. [42]

    HOVER: Versatile neural whole-body controller for humanoid robots

    Tairan He, Wenli Xiao, Toru Lin, Zhengyi Luo, Zhenjia Xu, Zhenyu Jiang, Jan Kautz, Changliu Liu, Guanya Shi, Xiaolong Wang, Linxi Fan, and Yuke Zhu. HOVER: Versatile neural whole-body controller for humanoid robots. InICRA, 2025. 24

    2025

  42. [43]

    Rgb-d mapping: Using kinect-style depth cameras for dense 3d modeling of indoor environments.IJRR, 31:647–663, 2012

    Peter Henry, Michael Krainin, Evan Herbst, Xiaofeng Ren, and Dieter Fox. Rgb-d mapping: Using kinect-style depth cameras for dense 3d modeling of indoor environments.IJRR, 31:647–663, 2012. 10, 25

    2012

  43. [44]

    M-LLM based video frame selection for efficient video understanding.arXiv preprint arXiv:2502.19680, 2025

    Kai Hu, Feng Gao, Xiaohan Nie, Peng Zhou, Son Tran, Tal Neiman, Lingyun Wang, Mubarak Shah, Raffay Hamid, Bing Yin, and Trishul Chilimbi. M-LLM based video frame selection for efficient video understanding.arXiv preprint arXiv:2502.19680, 2025. 10, 25

    arXiv 2025

  44. [45]

    3dllm-mem: Long-term spatial-temporal memory for embodied 3d large language model.CVPR, 2025

    Wenbo Hu, Yining Hong, Yanjun Wang, Leison Gao, Zibu Wei, Xingcheng Yao, Nanyun Peng, Yonatan Bitton, and Idan Szpektor. 3dllm-mem: Long-term spatial-temporal memory for embodied 3d large language model.CVPR, 2025. 24

    2025

  45. [46]

    Manipvqa: Injecting robotic affordance and physically grounded information into multi-modal large language models

    Siyuan Huang, Iaroslav Ponomarenko, Zhengkai Jiang, Xiaoqi Li, Xiaobin Hu, Peng Gao, Hongsheng Li, and Hao Dong. Manipvqa: Injecting robotic affordance and physically grounded information into multi-modal large language models. InIROS, 2024. 3

    2024

  46. [47]

    Inner monologue: Embodied reasoning through planning with language models.arXiv preprint arXiv:2207.05608, 2022

    Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, et al. Inner monologue: Embodied reasoning through planning with language models.arXiv preprint arXiv:2207.05608, 2022. 9

    Pith/arXiv arXiv 2022

  47. [48]

    𝜋0: A vision-language-action flow model for general robot control

    Physical Intelligence. 𝜋0: A vision-language-action flow model for general robot control. InRSS, 2025. 10, 24, 25

    2025

  48. [49]

    pi0.5: A vision-language-action model with open-world generalization.arXiv preprint arXiv:2504.16054, 2025

    Physical Intelligence. pi0.5: A vision-language-action model with open-world generalization.arXiv preprint arXiv:2504.16054, 2025. 9, 10, 25

    Pith/arXiv arXiv 2025

  49. [50]

    RoboBrain: A unified brain model for robotic manipulation from abstract to concrete

    Yuheng Ji, Huajie Tan, Jiayu Shi, Xiaoshuai Hao, Yuan Zhang, Hengyuan Zhang, Pengwei Wang, Mengdi Zhao, Yao Mu, Pengju An, Xinda Xue, Qinghang Su, Huaihai Lyu, Xiaolong Zheng, Jiaming Liu, Zhongyuan Wang, and Shanghang Zhang. RoboBrain: A unified brain model for robotic manipulation from abstract to concrete. InCVPR, 2025. 1, 3, 9, 25

    2025

  50. [51]

    Egotaskqa: Understanding human tasks in egocentric videos.Advances in Neural Information Processing Systems, 35:3343–3360, 2022

    Baoxiong Jia, Ting Lei, Song-Chun Zhu, and Siyuan Huang. Egotaskqa: Understanding human tasks in egocentric videos.Advances in Neural Information Processing Systems, 35:3343–3360, 2022. 5 13 Vesta: A Generalist Embodied Reasoning Model

    2022

  51. [52]

    Omnispatial: A comprehensive 3d spatial reasoning benchmark

    Mengdi Jia, Zekun Qi, Shaochen Zhang, Wenyao Zhang, Xinqiang Yu, Jiawei He, He Wang, and Li Yi. Omnispatial: A comprehensive 3d spatial reasoning benchmark. InICLR, 2026. 25

    2026

  52. [53]

    What’sup: An evaluation of spatial grounding in vision-language models

    Amita Kamath, Jack Hessel, and Kai-Wei Chang. What’sup: An evaluation of spatial grounding in vision-language models. InEMNLP, 2023. 25

    2023

  53. [54]

    Openvla: An open-source vision-language-action model.arXiv preprint arXiv:2406.09246, 2024

    Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, Quan Vuong, Thomas Kollar, Benjamin Burchfiel, Russ Tedrake, Dorsa Sadigh, Sergey Levine, Percy Liang, and Chelsea Finn. Openvla: An open-source vision-language-action model.arXiv preprint arXiv:2406.09246, 2...

    Pith/arXiv arXiv 2024

  54. [55]

    Beyond the nav-graph: Vision-and-language navigation in continuous environments

    Jacob Krantz, Erik Wijmans, Arjun Majumdar, Dhruv Batra, and Stefan Lee. Beyond the nav-graph: Vision-and-language navigation in continuous environments. InECCV, pages 104–120. Springer, 2020. 3, 10

    2020

  55. [56]

    Room-across-room: Multilingual vision-and-language navigation with dense spatiotemporal grounding

    Alexander Ku, Peter Anderson, Roma Patel, Eugene Ie, and Jason Baldridge. Room-across-room: Multilingual vision-and-language navigation with dense spatiotemporal grounding. InEMNLP, pages 4392–4412, 2020. 2, 3, 10

    2020

  56. [57]

    Pointpillars: Fast encoders for object detection from point clouds

    Alex H Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom. Pointpillars: Fast encoders for object detection from point clouds. InCVPR, 2019. 24

    2019

  57. [58]

    Molmoact: Action reasoning models that can reason in space.arXiv preprint arXiv:2508.07917, 2025

    Jason Lee, Jiafei Duan, Haoquan Fang, Yuquan Deng, Shuo Liu, Boyang Li, Bohan Fang, Jieyu Zhang, Yi Ru Wang, Sangho Lee, et al. Molmoact: Action reasoning models that can reason in space.arXiv preprint arXiv:2508.07917, 2025. 9

    Pith/arXiv arXiv 2025

  58. [59]

    Cronusvla: Towards efficient and robust manipulation via multi-frame vision-language-action modeling

    Hao Li, Shuai Yang, Yilun Chen, Xinyi Chen, Xiaoda Yang, Yang Tian, Hanqing Wang, Tai Wang, Dahua Lin, Feng Zhao, and Jiangmiao Pang. Cronusvla: Towards efficient and robust manipulation via multi-frame vision-language-action modeling. InAAAI, 2026. 10, 25

    2026

  59. [60]

    Novaflow: Zero-shot manipulation via actionable flow from generated videos

    Hongyu Li, Lingfeng Sun, Yafei Hu, Duy Ta, Jennifer Barry, George Konidaris, and Jiahui Fu. Novaflow: Zero-shot manipulation via actionable flow from generated videos. InICRA, 2026. 24

    2026

  60. [61]

    Aesbiasbench: Aesthetic and cultural bias evaluation.arXiv preprint arXiv:2509.11620, 2025

    Kun Li, Lai-Man Po, Hongzheng Yang, Xuyuan Xu, Kangcheng Liu, and Yuzhi Zhao. Aesbiasbench: Aesthetic and cultural bias evaluation.arXiv preprint arXiv:2509.11620, 2025. 25

    arXiv 2025

  61. [62]

    Qwen3-vl-embedding and qwen3-vl-reranker: A unifiedframeworkforstate-of-the-artmultimodalretrievalandranking.arXivpreprintarXiv:2601.04720,

    Mingxin Li, Yanzhao Zhang, Dingkun Long, Keqin Chen, Sibo Song, Shuai Bai, Zhibo Yang, Pengjun Xie, An Yang, Dayiheng Liu, Jingren Zhou, and Junyang Lin. Qwen3-vl-embedding and qwen3-vl-reranker: A unifiedframeworkforstate-of-the-artmultimodalretrievalandranking.arXivpreprintarXiv:2601.04720,

    Pith/arXiv arXiv

  62. [63]

    Robonurse-vla: Robotic scrub nurse system based on vision-language-action model

    Shunlei Li, Jin Wang, Rui Dai, Wanyu Ma, Wing Yin Ng, Yingbai Hu, and Zheng Li. Robonurse-vla: Robotic scrub nurse system based on vision-language-action model. InIROS, 2025. 24

    2025

  63. [64]

    CogVLA: Cognition-aligned vision-language- action model via instruction-driven routing & sparsification

    Wei Li, Renshan Zhang, Rui Shao, Jie He, and Liqiang Nie. CogVLA: Cognition-aligned vision-language- action model via instruction-driven routing & sparsification. InNeurIPS, 2025. 24

    2025

  64. [65]

    Robust navigation with language pretraining and stochastic sampling

    Xiujun Li, Chunyuan Li, Qiaolin Xia, Yonatan Bisk, Asli Celikyilmaz, Jianfeng Gao, Noah A Smith, and Yejin Choi. Robust navigation with language pretraining and stochastic sampling. InProceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNL...

    2019

  65. [66]

    Hamster: Hierarchical action models for open-world robot manipulation.arXiv preprint arXiv:2502.05485, 2025

    Yi Li, Yuquan Deng, Jesse Zhang, Joel Jang, Marius Memmel, Raymond Yu, Caelan Reed Garrett, Fabio Ramos, Dieter Fox, Anqi Li, Abhishek Gupta, and Ankit Goyal. Hamster: Hierarchical action models for open-world robot manipulation.arXiv preprint arXiv:2502.05485, 2025. 10, 24, 25 14 Vesta: A Generalist Embodied Reasoning Model

    arXiv 2025

  66. [67]

    Bfm- zero: Apromptablebehavioralfoundationmodelforhumanoidcontrolusingunsupervisedreinforcement learning.arXiv preprint arXiv:2511.04131, 2025

    Yitang Li, Zhengyi Luo, Tonghe Zhang, Cunxi Dai, Anssi Kanervisto, Andrea Tirinzoni, Haoyang Weng, Kris Kitani, Mateusz Guzek, Ahmed Touati, Alessandro Lazaric, Matteo Pirotta, and Guanya Shi. Bfm- zero: Apromptablebehavioralfoundationmodelforhumanoidcontrolusingunsupervisedreinforcement learning.arXiv preprint arXiv:2511.04131, 2025. 24

    arXiv 2025

  67. [68]

    Optimus-1: Hybrid multimodal memory empowered agents excel in long-horizon tasks.CVPR, 2025

    Zaijing Li, Yuquan Xie, Rui Shao, Gongwei Chen, Dongmei Jiang, and Liqiang Nie. Optimus-1: Hybrid multimodal memory empowered agents excel in long-horizon tasks.CVPR, 2025. 24

    2025

  68. [69]

    Huang, Luke Zettlemoyer, Dieter Fox, Yu Xiang, Anqi Li, Andreea Bobu, Abhishek Gupta, Stephen Tu, Erdem Biyik, and Jesse Zhang

    Anthony Liang, Yigit Korkmaz, Jiahui Zhang, Minyoung Hwang, Abrar Anwar, Sidhant Kaushik, Aditya Shah, Alex S. Huang, Luke Zettlemoyer, Dieter Fox, Yu Xiang, Anqi Li, Andreea Bobu, Abhishek Gupta, Stephen Tu, Erdem Biyik, and Jesse Zhang. Robometer: Scaling general-purpose robotic reward models via trajectory comparisons.arXiv preprint arXiv:2603.02115, 2026. 25

    Pith/arXiv arXiv 2026

  69. [70]

    Lawrence and Doll

    Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dollár. Microsoft coco: Common objects in context. In ECCV, pages 740–755. Springer International Publishing, 2014. doi: 10.1007/978-3-319-10602-1_48. 3

    work page doi:10.1007/978-3-319-10602-1_48 2014

  70. [71]

    Visual spatial reasoning.TACL, 2023

    Fangyu Liu, Guy Emerson, and Nigel Collier. Visual spatial reasoning.TACL, 2023. 25

    2023

  71. [72]

    RoboMamba: Efficient vision-language-action model for robotic reasoning and manipulation

    Jiaming Liu, Mengzhen Liu, Zhenyu Wang, Pengju An, Xiaoqi Li, Kaichen Zhou, Senqiao Yang, Renrui Zhang, Yandong Guo, and Shanghang Zhang. RoboMamba: Efficient vision-language-action model for robotic reasoning and manipulation. InNeurIPS, 2024. 24

    2024

  72. [73]

    Rdt-1b: a diffusion foundation model for bimanual manipulation

    Songming Liu, Lingxuan Wu, Bangguo Li, Hengkai Tan, Huayu Chen, Zhengyi Wang, Ke Xu, Hang Su, and Jun Zhu. Rdt-1b: a diffusion foundation model for bimanual manipulation. InICLR, 2025. 24

    2025

  73. [74]

    Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation

    Zhijian Liu, Haotian Tang, Alexander Amini, Xinyu Yang, Huizi Mao, Daniela Rus, and Song Han. Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation. InICRA, 2023. 24

    2023

  74. [75]

    Vision-language memory for spatial reasoning.arXiv preprint arXiv:2511.20644, 2025

    Zuntao Liu, Yi Du, Taimeng Fu, Shaoshu Su, Cherie Ho, and Chen Wang. Vision-language memory for spatial reasoning.arXiv preprint arXiv:2511.20644, 2025. 24

    arXiv 2025

  75. [76]

    A survey on vision-language- action models for embodied ai.arXiv preprint arXiv:2405.14093, 2024

    Yueen Ma, Zixing Song, Yuzheng Zhuang, Jianye Hao, and Irwin King. A survey on vision-language- action models for embodied ai.arXiv preprint arXiv:2405.14093, 2024. 24

    Pith/arXiv arXiv 2024

  76. [77]

    Actra: Optimized transformer architecture for vision-language-action models in robot learning

    Yueen Ma, Dafeng Chi, Shiguang Wu, Yuecheng Liu, Yuzheng Zhuang, and Irwin King. Actra: Optimized transformer architecture for vision-language-action models in robot learning. InEMNLP, 2025. 24

    2025

  77. [78]

    Argus: Vision-centric reasoning with grounded chain-of-thought

    Yunze Man, De-An Huang, Guilin Liu, Shiwei Sheng, Shilong Liu, Liang-Yan Gui, Jan Kautz, Yu-Xiong Wang, and Zhiding Yu. Argus: Vision-centric reasoning with grounded chain-of-thought. InCVPR, 2025. 24

    2025

  78. [79]

    Online episodic memory visual query localization with egocentric streaming object memory

    Zaira Manigrasso, Matteo Dunnhofer, Antonino Furnari, Moritz Nottebaum, Antonio Finocchiaro, Davide Marana, Rosario Forte, Giovanni Maria Farinella, and Christian Micheloni. Online episodic memory visual query localization with egocentric streaming object memory. InWACV, 2026. 10, 25

    2026

  79. [80]

    PhysWorld: Robot learning from a physical world model.arXiv preprint arXiv:2511.07416, 2025

    Jiageng Mao, Sicheng He, Hao-Ning Wu, Yang You, Shuyang Sun, Zhicheng Wang, Yanan Bao, Huizhong Chen, Leonidas Guibas, Vitor Guizilini, Howard Zhou, and Yue Wang. PhysWorld: Robot learning from a physical world model.arXiv preprint arXiv:2511.07416, 2025. 24

    arXiv 2025

  80. [81]

    Bpp: Long-context robot imitation learning by focusing on key history frames.arXiv preprint arXiv:2602.15010, 2026

    Max Sobol Mark, Jacky Liang, Maria Attarian, Chuyuan Fu, Debidatta Dwibedi, Dhruv Shah, and Aviral Kumar. Bpp: Long-context robot imitation learning by focusing on key history frames.arXiv preprint arXiv:2602.15010, 2026. 10, 25 15 Vesta: A Generalist Embodied Reasoning Model

    arXiv 2026

Showing first 80 references.