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arxiv: 2510.10125 · v3 · pith:7T3FOS2Rnew · submitted 2025-10-11 · 💻 cs.RO · cs.AI

Ctrl-World: A Controllable Generative World Model for Robot Manipulation

Yanjiang Guo , Lucy Xiaoyang Shi , Jianyu Chen , Chelsea Finn This is my paper

Pith reviewed 2026-05-16 01:08 UTC · model grok-4.3

classification 💻 cs.RO cs.AI
keywords world modelrobot manipulationgenerative modelpolicy fine-tuningimagination learningmulti-view predictionlong-horizon consistency
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The pith

A controllable world model ranks robot policies and improves them by 44.7 percent through imagined trajectories alone.

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

Generalist robot policies need extensive real-world testing on new objects and instructions, which is slow and expensive. This paper introduces Ctrl-World, a multi-view generative model that produces consistent long-horizon video rollouts conditioned on actions and camera poses. The model supports two direct uses: it ranks different policies by their imagined success rates without any physical trials, and it creates new training data by synthesizing successful trajectories for supervised fine-tuning. When policies are updated on these imagined successes, their real-world performance rises measurably. The approach therefore removes the need for large numbers of corrective robot experiments during policy development.

Core claim

The controllable multi-view world model maintains long-horizon consistency with a pose-conditioned memory retrieval mechanism and achieves precise action control through frame-level action conditioning. Trained on the DROID dataset of 95k trajectories across 564 scenes, the model generates spatially and temporally consistent trajectories under novel scenarios and new camera placements for over 20 seconds. These generated trajectories enable accurate ranking of policy performance without real-world robot rollouts. By synthesizing successful trajectories in imagination and using them for supervised fine-tuning, the approach improves policy success by 44.7 percent.

What carries the argument

The pose-conditioned memory retrieval mechanism paired with frame-level action conditioning inside a multi-view generative video model, which produces controllable long-horizon trajectories that serve as proxies for real dynamics.

If this is right

  • Policy selection and iteration can occur entirely inside imagination without physical robot time.
  • Corrective training data for new tasks can be generated at scale from a single trained world model.
  • Generalist policies can be adapted to novel objects and instructions more quickly than with real rollouts.
  • The same generated trajectories can serve both for evaluation and for direct supervised improvement.

Where Pith is reading between the lines

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

  • If the model generalizes further, it could support closed-loop planning and search inside imagination rather than only offline ranking.
  • Similar controllable world models could reduce data requirements across other embodied domains such as navigation or multi-robot coordination.
  • The 20-second horizon already demonstrated suggests the method may extend to longer tasks once memory retrieval is strengthened.

Load-bearing premise

The imagined trajectories remain accurate enough proxies for real-world dynamics on unseen objects, instructions, and camera views to support reliable policy ranking and effective fine-tuning.

What would settle it

Roll out the top-ranked policies from the world model in the real world and observe whether their success ordering matches the imagined ranking or whether fine-tuning on the synthesized trajectories produces the reported 44.7 percent gain.

read the original abstract

Generalist robot policies can now perform a wide range of manipulation skills, but evaluating and improving their ability with unfamiliar objects and instructions remains a significant challenge. Rigorous evaluation requires a large number of real-world rollouts, while systematic improvement demands additional corrective data with expert labels. Both of these processes are slow, costly, and difficult to scale. World models offer a promising, scalable alternative by enabling policies to rollout within imagination space. However, a key challenge is building a controllable world model that can handle multi-step interactions with generalist robot policies. This requires a world model compatible with modern generalist policies by supporting multi-view prediction, fine-grained action control, and consistent long-horizon interactions, which is not achieved by previous works. In this paper, we make a step forward by introducing a controllable multi-view world model that can be used to evaluate and improve the instruction-following ability of generalist robot policies. Our model maintains long-horizon consistency with a pose-conditioned memory retrieval mechanism and achieves precise action control through frame-level action conditioning. Trained on the DROID dataset (95k trajectories, 564 scenes), our model generates spatially and temporally consistent trajectories under novel scenarios and new camera placements for over 20 seconds. We show that our method can accurately rank policy performance without real-world robot rollouts. Moreover, by synthesizing successful trajectories in imagination and using them for supervised fine-tuning, our approach can improve policy success by 44.7\%.

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.

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper's core claims rest on training a controllable world model on the DROID dataset (95k trajectories) and then measuring downstream policy improvements via held-out real-world evaluations after supervised fine-tuning on synthesized trajectories. No load-bearing step reduces the reported 44.7% success gain, policy ranking accuracy, or long-horizon consistency to a fitted parameter or self-referential definition by construction; the evaluation metrics and improvement figures are obtained from separate experimental rollouts rather than being tautological with the model's training objective or internal equations.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that a generative model trained on DROID generalizes to novel scenarios sufficiently well to proxy real robot behavior; standard deep-learning training assumptions and dataset coverage are also required.

free parameters (1)
  • model architecture hyperparameters and training schedule
    Typical for large generative models; exact values not stated in abstract.
axioms (1)
  • domain assumption The DROID dataset and the proposed memory and conditioning mechanisms suffice for generalization to novel objects and viewpoints
    Invoked to support claims of usefulness on unfamiliar scenarios.

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Forward citations

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Reference graph

Works this paper leans on

56 extracted references · 56 canonical work pages · cited by 46 Pith papers · 32 internal anchors

  1. [1]

    Cosmos World Foundation Model Platform for Physical AI

    Niket Agarwal, Arslan Ali, Maciej Bala, Yogesh Balaji, Erik Barker, Tiffany Cai, Prithvijit Chat- topadhyay, Yongxin Chen, Yin Cui, Yifan Ding, et al. Cosmos world foundation model platform for physical ai.arXiv preprint arXiv:2501.03575,

    work page internal anchor Pith review Pith/arXiv arXiv

  2. [2]

    Roboarena: Distributed real-world evaluation of generalist robot policies

    Pranav Atreya, Karl Pertsch, Tony Lee, Moo Jin Kim, Arhan Jain, Artur Kuramshin, Clemens Eppner, Cyrus Neary, Edward Hu, Fabio Ramos, et al. Roboarena: Distributed real-world evaluation of generalist robot policies.arXiv preprint arXiv:2506.18123,

    work page arXiv

  3. [3]

    Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulation

    Homanga Bharadhwaj, Debidatta Dwibedi, Abhinav Gupta, Shubham Tulsiani, Carl Doersch, Ted Xiao, Dhruv Shah, Fei Xia, Dorsa Sadigh, and Sean Kirmani. Gen2act: Human video generation in novel scenarios enables generalizable robot manipulation.arXiv preprint arXiv:2409.16283,

    work page internal anchor Pith review Pith/arXiv arXiv

  4. [4]

    Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models

    Kevin Black, Mitsuhiko Nakamoto, Pranav Atreya, Homer Walke, Chelsea Finn, Aviral Kumar, and Sergey Levine. Zero-shot robotic manipulation with pretrained image-editing diffusion models. arXiv preprint arXiv:2310.10639,

    work page internal anchor Pith review arXiv

  5. [5]

    $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

    Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, et al. π0: A vision-language-action flow model for general robot control.arXiv preprint arXiv:2410.24164,

    work page internal anchor Pith review Pith/arXiv arXiv

  6. [6]

    Real-Time Execution of Action Chunking Flow Policies

    Kevin Black, Manuel Y Galliker, and Sergey Levine. Real-time execution of action chunking flow policies.arXiv preprint arXiv:2506.07339,

    work page internal anchor Pith review Pith/arXiv arXiv

  7. [7]

    Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

    Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram V oleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets.arXiv preprint arXiv:2311.15127, 2023a. Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanj...

    work page internal anchor Pith review Pith/arXiv arXiv

  8. [8]

    Wow: Towards a world omni- scient world model through embodied interaction,

    Xiaowei Chi, Peidong Jia, Chun-Kai Fan, Xiaozhu Ju, Weishi Mi, Kevin Zhang, Zhiyuan Qin, Wanxin Tian, Kuangzhi Ge, Hao Li, et al. Wow: Towards a world omniscient world model through embodied interaction.arXiv preprint arXiv:2509.22642,

    work page arXiv

  9. [9]

    doi:10.48550/arXiv.2505.03912 , abstract =

    11 Published as a conference paper at ICLR 2026 Can Cui, Pengxiang Ding, Wenxuan Song, Shuanghao Bai, Xinyang Tong, Zirui Ge, Runze Suo, Wanqi Zhou, Yang Liu, Bofang Jia, et al. Openhelix: A short survey, empirical analysis, and open-source dual-system vla model for robotic manipulation.arXiv preprint arXiv:2505.03912,

    work page arXiv 2026

  10. [10]

    RoboNet: Large-Scale Multi-Robot Learning

    Sudeep Dasari, Frederik Ebert, Stephen Tian, Suraj Nair, Bernadette Bucher, Karl Schmeckpeper, Siddharth Singh, Sergey Levine, and Chelsea Finn. Robonet: Large-scale multi-robot learning. arXiv preprint arXiv:1910.11215,

    work page internal anchor Pith review arXiv 1910

  11. [11]

    Vision-language models as success detectors,

    Yuqing Du, Ksenia Konyushkova, Misha Denil, Akhil Raju, Jessica Landon, Felix Hill, Nando De Freitas, and Serkan Cabi. Vision-language models as success detectors.arXiv preprint arXiv:2303.07280,

    work page arXiv

  12. [12]

    Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control

    Frederik Ebert, Chelsea Finn, Sudeep Dasari, Annie Xie, Alex Lee, and Sergey Levine. Visual foresight: Model-based deep reinforcement learning for vision-based robotic control.arXiv preprint arXiv:1812.00568,

    work page internal anchor Pith review Pith/arXiv arXiv

  13. [13]

    Vidar: Embodied Video Diffusion Model for Generalist Manipulation

    Yao Feng, Hengkai Tan, Xinyi Mao, Guodong Liu, Shuhe Huang, Chendong Xiang, Hang Su, and Jun Zhu. Vidar: Embodied video diffusion model for generalist bimanual manipulation.arXiv preprint arXiv:2507.12898,

    work page internal anchor Pith review arXiv

  14. [14]

    Flip: Flow- centric generative planning as general-purpose manipulation world model,

    Chongkai Gao, Haozhuo Zhang, Zhixuan Xu, Zhehao Cai, and Lin Shao. Flip: Flow-centric generative planning as general-purpose manipulation world model.arXiv preprint arXiv:2412.08261,

    work page arXiv

  15. [15]

    Adaworld: Learning adaptable world models with latent actions.arXiv preprint arXiv:2503.18938, 2025

    Shenyuan Gao, Siyuan Zhou, Yilun Du, Jun Zhang, and Chuang Gan. Adaworld: Learning adaptable world models with latent actions.arXiv preprint arXiv:2503.18938,

    work page arXiv

  16. [16]

    Improving vision-language-action model with online reinforcement learning.arXiv preprint arXiv:2501.16664,

    Yanjiang Guo, Jianke Zhang, Xiaoyu Chen, Xiang Ji, Yen-Jen Wang, Yucheng Hu, and Jianyu Chen. Improving vision-language-action model with online reinforcement learning.arXiv preprint arXiv:2501.16664,

    work page arXiv

  17. [17]

    Dream to Control: Learning Behaviors by Latent Imagination

    Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi. Dream to control: Learning behaviors by latent imagination.arXiv preprint arXiv:1912.01603,

    work page internal anchor Pith review Pith/arXiv arXiv 1912

  18. [18]

    Mastering Atari with Discrete World Models

    Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, and Jimmy Ba. Mastering atari with discrete world models.arXiv preprint arXiv:2010.02193,

    work page internal anchor Pith review Pith/arXiv arXiv 2010

  19. [19]

    Training Agents Inside of Scalable World Models

    Danijar Hafner, Wilson Yan, and Timothy Lillicrap. Training agents inside of scalable world models. arXiv preprint arXiv:2509.24527,

    work page internal anchor Pith review Pith/arXiv arXiv

  20. [20]

    Temporal difference learning for model predictive control.arXiv preprint arXiv:2203.04955, 2022

    Nicklas Hansen, Xiaolong Wang, and Hao Su. Temporal difference learning for model predictive control.arXiv preprint arXiv:2203.04955,

    work page arXiv

  21. [21]

    Pre-trained video generative models as world simulators.CoRR, abs/2502.07825, February 2025

    Haoran He, Yang Zhang, Liang Lin, Zhongwen Xu, and Ling Pan. Pre-trained video generative models as world simulators.arXiv preprint arXiv:2502.07825,

    work page arXiv

  22. [22]

    Image quality metrics: Psnr vs

    12 Published as a conference paper at ICLR 2026 Alain Hore and Djemel Ziou. Image quality metrics: Psnr vs. ssim. In2010 20th international conference on pattern recognition, pp. 2366–2369. IEEE,

    work page 2026

  23. [23]

    Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations

    Yucheng Hu, Yanjiang Guo, Pengchao Wang, Xiaoyu Chen, Yen-Jen Wang, Jianke Zhang, Koushil Sreenath, Chaochao Lu, and Jianyu Chen. Video prediction policy: A generalist robot policy with predictive visual representations.arXiv preprint arXiv:2412.14803,

    work page internal anchor Pith review Pith/arXiv arXiv

  24. [24]

    Particleformer: A 3d point cloud world model for multi-object, multi-material robotic manipulation.arXiv preprint arXiv:2506.23126,

    Suning Huang, Qianzhong Chen, Xiaohan Zhang, Jiankai Sun, and Mac Schwager. Particleformer: A 3d point cloud world model for multi-object, multi-material robotic manipulation.arXiv preprint arXiv:2506.23126,

    work page arXiv

  25. [25]

    $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization

    Physical Intelligence, Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, et al. pi {0.5}: a vision-language- action model with open-world generalization.arXiv preprint arXiv:2504.16054,

    work page internal anchor Pith review Pith/arXiv arXiv

  26. [26]

    arXiv preprint arXiv:2505.09723 (2025)

    Yuxin Jiang, Shengcong Chen, Siyuan Huang, Liliang Chen, Pengfei Zhou, Yue Liao, Xindong He, Chiming Liu, Hongsheng Li, Maoqing Yao, et al. Enerverse-ac: Envisioning embodied environments with action condition.arXiv preprint arXiv:2505.09723,

    work page arXiv

  27. [27]

    DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

    Alexander Khazatsky, Karl Pertsch, Suraj Nair, Ashwin Balakrishna, Sudeep Dasari, Siddharth Karamcheti, Soroush Nasiriany, Mohan Kumar Srirama, Lawrence Yunliang Chen, Kirsty Ellis, et al. Droid: A large-scale in-the-wild robot manipulation dataset.arXiv preprint arXiv:2403.12945,

    work page internal anchor Pith review Pith/arXiv arXiv

  28. [28]

    OpenVLA: An Open-Source Vision-Language-Action Model

    Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, et al. Openvla: An open-source vision-language-action model.arXiv preprint arXiv:2406.09246,

    work page internal anchor Pith review Pith/arXiv arXiv

  29. [29]

    Unified Video Action Model

    Shuang Li, Yihuai Gao, Dorsa Sadigh, and Shuran Song. Unified video action model.arXiv preprint arXiv:2503.00200, 2025a. Xuanlin Li, Kyle Hsu, Jiayuan Gu, Karl Pertsch, Oier Mees, Homer Rich Walke, Chuyuan Fu, Ishikaa Lunawat, Isabel Sieh, Sean Kirmani, et al. Evaluating real-world robot manipulation policies in simulation.arXiv preprint arXiv:2405.05941,

    work page internal anchor Pith review Pith/arXiv arXiv

  30. [30]

    Worldeval: World model as real-world robot policies evaluator.arXiv preprint arXiv:2505.19017, 2025

    Yaxuan Li, Yichen Zhu, Junjie Wen, Chaomin Shen, and Yi Xu. Worldeval: World model as real-world robot policies evaluator.arXiv preprint arXiv:2505.19017, 2025b. Junbang Liang, Ruoshi Liu, Ege Ozguroglu, Sruthi Sudhakar, Achal Dave, Pavel Tokmakov, Shuran Song, and Carl V ondrick. Dreamitate: Real-world visuomotor policy learning via video generation. arX...

    work page arXiv

  31. [31]

    Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation

    Yue Liao, Pengfei Zhou, Siyuan Huang, Donglin Yang, Shengcong Chen, Yuxin Jiang, Yue Hu, Jingbin Cai, Si Liu, Jianlan Luo, et al. Genie envisioner: A unified world foundation platform for robotic manipulation.arXiv preprint arXiv:2508.05635,

    work page internal anchor Pith review Pith/arXiv arXiv

  32. [32]

    HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model

    Jiaming Liu, Hao Chen, Pengju An, Zhuoyang Liu, Renrui Zhang, Chenyang Gu, Xiaoqi Li, Ziyu Guo, Sixiang Chen, Mengzhen Liu, et al. Hybridvla: Collaborative diffusion and autoregression in a unified vision-language-action model.arXiv preprint arXiv:2503.10631,

    work page internal anchor Pith review Pith/arXiv arXiv

  33. [33]

    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.arXiv preprint arXiv:2410.07864,

    work page internal anchor Pith review Pith/arXiv arXiv

  34. [34]

    Foundation reward models for general robot skill acquisition

    Yecheng Jason Ma. Foundation reward models for general robot skill acquisition. InRobotics: Science and Systems-Pioneers Workshop 2025,

    work page 2025

  35. [35]

    Deep dynamics models for learning dexterous manipulation

    13 Published as a conference paper at ICLR 2026 Anusha Nagabandi, Kurt Konolige, Sergey Levine, and Vikash Kumar. Deep dynamics models for learning dexterous manipulation. InConference on robot learning, pp. 1101–1112. PMLR,

    work page 2026

  36. [36]

    FAST: Efficient Action Tokenization for Vision-Language-Action Models

    Karl Pertsch, Kyle Stachowicz, Brian Ichter, Danny Driess, Suraj Nair, Quan Vuong, Oier Mees, Chelsea Finn, and Sergey Levine. Fast: Efficient action tokenization for vision-language-action models.arXiv preprint arXiv:2501.09747,

    work page internal anchor Pith review Pith/arXiv arXiv

  37. [37]

    Quevedo, A

    Julian Quevedo, Percy Liang, and Sherry Yang. Evaluating robot policies in a world model.arXiv preprint arXiv:2506.00613,

    work page arXiv

  38. [38]

    Cosmos-drive- dreams: Scalable synthetic driving data generation with world foundation models.arXiv preprint arXiv:2506.09042,

    Xuanchi Ren, Yifan Lu, Tianshi Cao, Ruiyuan Gao, Shengyu Huang, Amirmojtaba Sabour, Tianchang Shen, Tobias Pfaff, Jay Zhangjie Wu, Runjian Chen, et al. Cosmos-drive-dreams: Scalable synthetic driving data generation with world foundation models.arXiv preprint arXiv:2506.09042,

    work page arXiv

  39. [39]

    Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models

    Lucy Xiaoyang Shi, Brian Ichter, Michael Equi, Liyiming Ke, Karl Pertsch, Quan Vuong, James Tanner, Anna Walling, Haohuan Wang, Niccolo Fusai, et al. Hi robot: Open-ended instruction following with hierarchical vision-language-action models.arXiv preprint arXiv:2502.19417,

    work page internal anchor Pith review Pith/arXiv arXiv

  40. [40]

    AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation

    Hengkai Tan, Yao Feng, Xinyi Mao, Shuhe Huang, Guodong Liu, Zhongkai Hao, Hang Su, and Jun Zhu. Anypos: Automated task-agnostic actions for bimanual manipulation.arXiv preprint arXiv:2507.12768,

    work page internal anchor Pith review Pith/arXiv arXiv

  41. [41]

    Gemini: A Family of Highly Capable Multimodal Models

    URL https://www. 1x.tech/1x-world-model.pdf. Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models.arXiv preprint arXiv:2312.11805,

    work page internal anchor Pith review Pith/arXiv arXiv

  42. [42]

    Towards Accurate Generative Models of Video: A New Metric & Challenges

    Thomas Unterthiner, Sjoerd Van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, and Sylvain Gelly. Towards accurate generative models of video: A new metric & challenges.arXiv preprint arXiv:1812.01717,

    work page internal anchor Pith review Pith/arXiv arXiv

  43. [43]

    Wan: Open and Advanced Large-Scale Video Generative Models

    Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, et al. Wan: Open and advanced large-scale video generative models. arXiv preprint arXiv:2503.20314,

    work page internal anchor Pith review Pith/arXiv arXiv

  44. [44]

    DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control

    Junjie Wen, Yichen Zhu, Jinming Li, Zhibin Tang, Chaomin Shen, and Feifei Feng. Dexvla: Vision-language model with plug-in diffusion expert for general robot control.arXiv preprint arXiv:2502.05855,

    work page internal anchor Pith review Pith/arXiv arXiv

  45. [45]

    Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight

    14 Published as a conference paper at ICLR 2026 Annie Xie, Frederik Ebert, Sergey Levine, and Chelsea Finn. Improvisation through physical understanding: Using novel objects as tools with visual foresight.arXiv preprint arXiv:1904.05538,

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  46. [46]

    Learning Interactive Real-World Simulators

    Mengjiao Yang, Yilun Du, Kamyar Ghasemipour, Jonathan Tompson, Dale Schuurmans, and Pieter Abbeel. Learning interactive real-world simulators.arXiv preprint arXiv:2310.06114, 1(2):6,

    work page internal anchor Pith review arXiv

  47. [47]

    Hirt: Enhancing robotic control with hierarchical robot transformers.arXiv preprint arXiv:2410.05273,

    Jianke Zhang, Yanjiang Guo, Xiaoyu Chen, Yen-Jen Wang, Yucheng Hu, Chengming Shi, and Jianyu Chen. Hirt: Enhancing robotic control with hierarchical robot transformers.arXiv preprint arXiv:2410.05273,

    work page arXiv

  48. [48]

    Up-vla: A unified understanding and prediction model for embodied agent.arXiv preprint arXiv:2501.18867, 2025a

    Jianke Zhang, Yanjiang Guo, Yucheng Hu, Xiaoyu Chen, Xiang Zhu, and Jianyu Chen. Up-vla: A unified understanding and prediction model for embodied agent.arXiv preprint arXiv:2501.18867,

    work page arXiv

  49. [49]

    Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

    Tony Z Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn. Learning fine-grained bimanual manipulation with low-cost hardware.arXiv preprint arXiv:2304.13705,

    work page internal anchor Pith review Pith/arXiv arXiv

  50. [50]

    FLARE: Robot Learning with Implicit World Modeling

    Ruijie Zheng, Jing Wang, Scott Reed, Johan Bjorck, Yu Fang, Fengyuan Hu, Joel Jang, Kaushil Kundalia, Zongyu Lin, Loic Magne, et al. Flare: Robot learning with implicit world modeling. arXiv preprint arXiv:2505.15659,

    work page internal anchor Pith review arXiv

  51. [51]

    Flowvla: Visual chain of thought-based motion reasoning for vision-language-action models.arXiv preprint arXiv:2508.18269, 2025

    Zhide Zhong, Haodong Yan, Junfeng Li, Xiangchen Liu, Xin Gong, Wenxuan Song, Jiayi Chen, and Haoang Li. Flowvla: Thinking in motion with a visual chain of thought.arXiv preprint arXiv:2508.18269,

    work page arXiv

  52. [52]

    Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets

    Chuning Zhu, Raymond Yu, Siyuan Feng, Benjamin Burchfiel, Paarth Shah, and Abhishek Gupta. Unified world models: Coupling video and action diffusion for pretraining on large robotic datasets. arXiv preprint arXiv:2504.02792,

    work page internal anchor Pith review Pith/arXiv arXiv

  53. [53]

    Irasim: Learning interactive real-robot action simulators

    Fangqi Zhu, Hongtao Wu, Song Guo, Yuxiao Liu, Chilam Cheang, and Tao Kong. Irasim: Learning interactive real-robot action simulators.arXiv preprint arXiv:2406.14540,

    work page arXiv

  54. [54]

    More videos can be found at the anonymous website: https://sites.google.com/view/ ctrl-world

    15 Published as a conference paper at ICLR 2026 Code can be found in the Supplementary Materials. More videos can be found at the anonymous website: https://sites.google.com/view/ ctrl-world. A MOREDETAILS FORWORLDMODELLEARNING Model Architecture.Our world model closely follows the architecture of Stable Video Diffusion (SVD) (Blattmann et al., 2023a), an...

    work page 2026

  55. [55]

    The learning rate is set to be 1e-5, and we train for 100k steps, which takes approximately 2–3 days to complete. B MOREDETAILS FORPOLICYEVALUATION Details on interaction between policy and world model.We directly use the offi- cial π0-DROID, π0-FAST-DROID, and π0.5-DROIDpolicies from https://github.com/ Physical-Intelligence/openpi to interact with Ctrl-...

    work page 2026

  56. [56]

    Pick up A and place in B

    Task details and criterion.In our experiments, we use human annotators to evaluate whether each trajectory is a success or a failure. Although this evaluation process can be automated in the future using large vision-language reward models, our focus in this paper is on the world model itself, so we rely on human preference as the reward signal. We provid...

    work page 2026