---
title: 【DL輪読会】Ctrl-World: A Controllable Generative World Model for Robot Manipulation
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author: [Deep Learning JP](https://convert.docswell.com/user/DeepLearning2023)
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description: 【DL輪読会】Ctrl-World: A Controllable Generative World Model for Robot Manipulation by Deep Learning JP
published: February 12, 26
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---
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DEEP LEARNING JP
[DL Papers]
Ctrl-World: A Controllable Generative World Model for
Robot Manipulation
Shu MORIKUNI, Matsuo-Iwasawa Lab
http://deeplearning.jp/
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Bibliography Information
Title
Authors
Ctrl-World: A Controllable Generative World Model for Robot Manipulation
Yanjiang Guo, Lucy Xiaoyang Shi, Jianyu Chen, Chelsea Finn
Affliations
Stanford University, Tsinghua University
Publication
arXiv preprint (2025/10/15)
Arxiv
Project Page
GitHub
Summary
https://arxiv.org/abs/2510.10125
https://ctrl-world.github.io/
https://github.com/Robert-gyj/Ctrl-World
A controllable multi-view synthesis world model that is used for generalist robot policy evaluation &amp;
performance improvement.
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When Video Generation Models Meet Robotic Models
2014
2012
GAN
AlexNet
2016
AlphaGo
StableDiffusion
2018
2019
2020
Transformer
GPT-1
SytleGan
DALL-E
Runway
2022
2023
ChatGPT
RT-1
2017
Sora
2024
Gemini
Video Generation Model
Cosmos
2025
RT-2
OpenVLA
Pi0.5
2026
Robotic Model
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Related Works &amp; Trend
Peer-reiewed Works
• [ICLR2025 Paper] https://leobarcellona.github.io/DreamToManipulate/
• **[ICCV2025 Paper] https://gen-irasim.github.io/
• [CoRL2025 Workshop] https://research.nvidia.com/labs/gear/dreamgen/
• [CoRL2025 Workshop] https://research.nvidia.com/labs/gear/flare/
Preprint Works
• [Arxiv Preprint 2025-12] https://dream2flow.github.io/
• [Arxiv Preprint 2025-09] https://world-model-eval.github.io/abstract
• [Arxiv Preprint 2026-01] https://point-world.github.io/
• [Arxiv Preprint 2026-01] https://research.nvidia.com/labs/dir/plenopticdreamer/
• [Arxiv Preprint 2026-02] https://dreamdojo-world.github.io/
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New Topic Added to a Top-tier Robotic Conference This Year
Video models and latent world models for robot learning
https://www.corl.org/contributions/call-for-papers
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Quick Demo
URL: https://ctrl-world.github.io/
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Overview
A controllable multi-view synthesis world-model which can be used for
(1) robot policy evaluation and (2) improve downstream policy performance.
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Background
The Difficulty in Effectively Scaling Real-world Generalist Robot Policy Evaluation and Performance
Generalist robot policies can perform wide range of manipulation tasks.
But evaluating and improving their ability with novel objects and instructions remain a significant challenge.
Systematic improvement demands additional expert labels.
Rigorous evaluation requires a large number of real-world rollouts.
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Challenge
The challenge is building a
controllable world model to handle multi-step interactions with generalist robot policies.
• Controllable world model is not new, what is the challenge?
• Most approaches are not sufficient to actively interact with generalist robot policies.
A.K.A policy-in-the-loop rollouts.
• Most models typically simulate only a single third-person camera view.
• Single-view input is also incompatible with many modern VLA policies.
• Existing models lack the fine-grained control required for high-frequency actions.
• They struggle to maintain temporal consistency across long-horizon video generations.
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Contributions
1. Long-horizon Consistency
Ctrl-World show that it generalizes to novel scenes and camera placements, sustaining coherent rollouts for
over 20 seconds.
2. Pose-conditioned Memory Retrieval
Ctrl-World reflect policies’ real-world “instruction-following” ability.
3. Downstream Robot Policy Improvement
Ctrl-World can be used to improve the performance of π0.5-DROID (Intelligence et al., 2025) on downstream
tasks with unseen objects and novel instructions by 44.7%.
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Proposed Approach
1. Multi-view Joint Predictions
Captures comprehensive visual representation of the scene paired with VLA policies, reduces hallucinations during contactrich object interactions.
2. Pose-conditioned Memory Retrieval
Aligns visual dynamics with control signals, ensuring that generated rollouts reflect the causal effect of each action.
3. Frame-level Action Conditioning
Adds sparse history frames into the context and projects corresponding pose information into each frame , This stabilizes longhorizon rollouts and preserves temporal consistency.
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Proposed Approach
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Problem Formulation
Modern Generalist Robot Policy ( π )
The Goal of Crtl-World Model ( W )
The Training Objective of Crtl-World Model ( W )
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Experimental Setup
1. DROID Platform
1.
95k trajectories in total (76k successful + 19k failed trajectories).
2.
564 scenes.
2. DROID Dataset
1.
95k trajectories in total (76k successful + 19k failed trajectories).
2.
564 scenes.
DROID Dataset [URL]
3. Training Details
1.
192x320 resolution
2.
7 frames of historical window with an interval of 1-2 seconds.
3.
15 step action chunk size ~= 1 second action chunk in DROID.
4.
2x8 H100 GPUs, 64 batch size for 2-3 days.
DROID Platform used in this work [URL]
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Ctrl-World Quantitative Result
Quantitative results for interactive long-trajectory generation
*PSNR (Peak Signal-to-Noise Ratio)
*SSIM (Structural Similarity Index)
*LPIPS (Learned Perceptual Image Patch Similarity)
*FID (Fréchet Inception Distance)
*FVD (Fréchet Video Distance)
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Ctrl-World Ablation Study Result
Ablation Study without (1) memory retrieval (2) frame-level condition (3) muti-view
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Ctrl-World Qualitative Result
Controllability contributed by memory retrieval &amp; frame-level pose condition
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Ctrl-World Qualitative Result
Consistency contributed by multi-view &amp; memory retrieval
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Correlation Result with Real-world Evaluation
Quantitative correlations between real-world and world-model rollouts
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Real-world Correlation Result
Inconsistent Tasks Example “close the laptop”
Ground-truth
Prediction
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Policy Improvement Algorithm
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Policy Improvement Result
Post-training result of the VLA model tested on unfamiliar objects and novel instructions
1. Rollout diversity is encouraged by
1. Rephrasing task instructions.
2. Resetting with new initial state.
2. 400 trajectories per task are generated.
3. 25–50 successful trajectories are retained based on
human preference judgments.
4. Translates to 6%~12% sampling efficiency
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Policy Improvement Result
Baseline Policy
Post-trained Policy
1. Pick the object
in top left side and
place in box.
2. Pick glove and
place in box.
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Summary
Summary
•
CTRL-WORLD provides multi-view, controllable generative world model designed specifically for closed-loop
interactions with modern VLA policies, which shows promising potential for both policy evaluation and policy
improvement.
Limitations
•
Complex physics, (e.g., collisions, objects sliding away, rotations)
•
Retrying scenarios.
Final Thought
•
Expectation on more known “priors” to be leveraged in solving physic consistency.
•
Encouragement of leveraging failed trajectories and emphasis on recovery.
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