Ctrl-World: A Controllable Generative World Model for Robot Manipulation
Authors: Yanjiang Guo
Lucy Xiaoyang Shi
Jianyu Chen
Chelsea Finn
Venue: ICLR 2026 Poster (2026)
Keywords: World Model
Vision-Language-Action Model (VLA)
Primary area: applications to robotics, autonomy, planning
TL;DR: We propose a controllable world model for robot manipulation, which can be used for policy evaluation and policy improvement.

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\%. Videos can be found at https://sites.google.com/view/ctrl-world.

Published as a conference paper at ICLR 2026
A CONTROLLABLE GENERATIVE WORLD MODEL FOR ROBOT MANIPULATION
Yanjiang Guo Lucy Xiaoyang Shi Jianyu Chen², Chelsea Finn'
Equal Contribution. Stanford University, 2 Tsinghua University Project page: https: ctt///trll- world. gi github. io
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 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 nuet prediction, fine- ane-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 iestruction-followeee ability of generalist robot policies. Our model maintains long-horizon consistency with a cose-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 linettunin, our approach can improve policy success by 44.7%.
INTRODUCTION
Recent advances in lision-language-action (VLA) models have demonstrated competence across a wide range of manipulation tasks and scenarios (Black et al. [024] Wen et al. 2025 Brohan
et al. 2023 Kim et al. 2024 Cui et al. 2025 Guo al. 2025 Zhang et al.) (024). Despite their promise, current policies remain brittle when tested in open-world circumstances (Shi et al. 2025).
central challenge is policy evaluation. Assessing generalist policy performance typically requires large numbers of real-world rollouts, carefully repeated across tasks and environments to achieve statistical significance (Atreya et al. (025).. Such protocols are logistically demanding, slow down iteration, and inhibit nuanced understanding of current policy capabilities. Equally critical is policy improvement: once weaknesses are revealed, existing methods offer few ways to strengthen policies on failure cases beyond collecting more expert data. Although large-scale pretraining provides some robustness, policies often remain fragile when they encounter unfamiliar objects or instructions. What is missing is a fast and cheap feedback-dring mechanism for refining generalist models: a way to surface failure cases, gather corrective experiences, and iteratively improve the policy.
Learning a predictive model and iterating in imagination is a scalable and promising alternative. While prior work has explored action artion-conditioned world models, most approaches focus on passive video prediction settings and are not sufficient to actively interact with advanced generalist policies (Li cet al. 2025b Zhu et al.) 2024). We observe several important limitations that hinder their ability to support policy-in-the-log rollouts. First, these models typically simulate only single third-person camera view, which can lead to severe partial observability and, in turn, cause hallucinations (e.g., an object snapping into the gripper without prior physical contact). This single-view input is also incompatible with many modern VLA policies that require both hird-ppesson and wrist-view cameras as input. Moreover, existing models typically lack the fine- grained control required to capture the

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Policy-in-thelloop world model rollout
Policy Evaluation
XN
Instru
Cam
Cam
Eam
Pred
Pred
Pred 3
Policy Improvement
Generalist Policy
World Model
Synthetic Trajectory
+44.7% 44%
Memory
step action chunk
Average Success Rate
Ctrl-Worid is designed for policy-in-the-lopp rollouts with generalist robot policies. It generates joint multi-view predictions (including wrist views), enforces fine aine-grained action control via frame-level conditioning, and sustains coherent memory retrieval. These components enable (1) accurate policy evaluation in imagination, with alignment to real-world rollouts, and (2) targeted policy improvement through synthetic trajectories.
Figure 1:
dynamics through pose cose-conditioned
causal effects of high-frequency actions. Finally, they struggle to maintain temporal consistency across long-horizon video generations.
In this paper, we introduce Ctrl-World, a Controllable, multi-view generative world model designed for policy-in-the-loop interaction, enabling multi-step rollouts entirely within imagination space, as illustrated in Figure T Our design relies three key components: (1) Joint multi-view prediction captures a more comprehensive visual representation of the scene and meets the input format of modern VLA policies. Notably, the inclusion of wrist-camera prediction significantly reduces hallucinations during contact-rich object interactions. (2) Frame-level action conditioning tightly aligns visual dynamics with control signals, ensuring that generated rollouts reflect the causal effect of each action. (3) Memory retrieval, which adds sparse history frames into the context and projects corresponding pose information into each frame, allows the model to attend to similar past states and retrieve relevant information. This mechanism stabilizes long- morizori rollouts and preserves temporal consisteney. Together, these mechanisms allow us to transform a pre-trained passive video generator into a comppabbbe interactive simulator.
The core contribution of this work is a controllable world model for robot manipulation. In experi- ments, we find this model enables new imagination-baser based workflow in which policies can be both evaluated with ranking alignment to real-world rollouts and improved improved-thruugh -thouugh targeted synthetic data that boosts success rates. Specifically, we train Ctrl-World on the DROID dataset (Khazatsky eet al. 2024) and show that it generalizes to novel scenes and camera placements, sustaining coherent rollouts for over 20 seconds. We further show that imagination based evaluations with Ctrl-World faithfully refeect policies' real-world instruction following ability. Finally, we demonstrate that we can improve the performance of -o.s-DRo1n (Intelligence et al. 2025) on downstream tasks with unseen objects and novel instructions by synthesizing successful trajectories inside the world model and performing supervised fine-tuning with these synthetic roll-outs.
cortuctioonfollwiing
2 RELATED WORKS
Video Generation Models for Robotics. Recent advances in video generation models (Agarwal et al. 2025 Wan et al. 2025 Blattmann al. 2023a Chi et al. (025) have enabled the creation of realistic and temporally consistent content, reflecting a strong understanding of the physical world. Some works leverage video prediction models to synthesize robotic trajectories with fake action labels, and these synthetic trajectories can then be used for policy learning (Jang et al., 20255 Bharadhwajj et al.
2024). Other works directly employ video models as policy backbones, decoding actions through
tracking or inverse dynamics (Black et al.) 2023 Du et al. 2024 Yang et al. 2023) Hu et al.) 2024
Liang et al., 2024: Liao et al. 2025 Tan et al. [2225] Feng et al. 2025). A complementary line of
research integrates future-prediction objectives into generalist policies via co-training (Zhao et al.
2025 Li et al. 2025al Zhu et al. 2025 Guo et al. 2024 Gao et al. 2024| Zhang et al., | Zheng
et al. 2025: Zhong et al. 2025) incorporating physical knowledge into the policy. Unlike these
works, we leverage video generation to perform action- cition-coniitioned prediction, which enables using
the model for both policy evaluation and policy improvement.
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Action-Conditioned World Models. Although pretrained video models powerful, they are often only conditioned on high-level language instructions. Nonetheless, some prior works have explored using oeciin-condditoned predictive models, both in low-dimensional state spaces (Nagabandi et al.)
2020) and with image observations Hafner et al. 2019: 2020); Hansen et al. 2022); Wu et al. (2023); Oh et al. (2015); Huang et al. (2025). Many of these approaches learn task specific models (Hamer
ct al., 2019), while we focus on training generalist, multi-task world models. Building on early
works
(Finn& & Levine 2017 Ebert al. 2018 Xie et al. 2019; Dasari et al. 2019 Yang al.
2023
Wu et al. 2024) as well as more recent approaches that leverage diffusion (Quevedo et al.
2025 Chen al. 2024 Ball et al. 2025 Gao al. 2025 Ren al.. 2025 Hafner et al. 2025) and
frame-level action conditioning (Zhu et al. 2024), we propose a model that incorporates multi-view prediction, long-horizon temporal coherence, and fine-grained controllability. Our experiments show that these capabilities enable effective evaluation and improvement of state-of-the-art generalist VIAA policies.
3 PROBLEM FORMULATION
We aim to develop a world model that can predict the future outcomes of actions proposed by a generalist robot policy. A modern generalist policy typically maps multi-view observations and language instructions into a sequence of actions (Zhao et al. [0233 Black et al. (025). Specifically, robot observation o, = [1] . Ie q:] includes n camera views Ii...., 4 and robot pose It, the policy outputs an H-step action chunk given an instruction l:
a++1, at42, G++H ~ I(-)0..))
Our goal is to use a world model W to predict the outcomes of executing each step in A, = [0c+1s--.. ".......,,... (c+H) H] To enable multi-step interaction with the policy in imagination space, W must
generate future multi-view observations:
Oc+1 O++H W(+oo,, At)
Then the final prediction Ot+ be send back to policy to produce the next action chunk A++H x-||0t+H, l). In this way, the policy and world model interact auto-regressively, enabling long-horizon rollouts entirely within imagination space.
CONTROLLABLE WORLD MODEL FOR ROBOT MANIPULATION
4.1 LEARNING WORLD MODEL CTRL-WORLD
Our goal is to learn world model that can be used to evaluate and improve modern VLA policies. To achieve this, the model must first support multiview observations that are commonly used by such policies. It is also important for the model to be controllable reliably and closely follow the action inputs - even when initialized from a pre-trained backbone that lacks such control. Finally, the model must maintain temporal consistency over long horizons, even in the presence of occlusions, to produce coherent rollouts. We initialize our world model from a pretrained video diffusion backbone with apatial-temporea transformers (Blattmann et al. 2023b and introduce three key adaptations, illustrated in Figure 2
Multi- Joint Predictions. State-of-the-art VLA models often rely on multiple third-person cameras for global context and mounted cameras for precise interactions (Intelligence et al.
2025 Liu al. 2024, 2025). To match this, the world model must generate spatially consistent predictions across all views at each step (Jiang et al. 2025). Prior work has shown that feed-forward transformers can effectively capture spatial relationships between multi-view cameras
scalable manner (Wang al., 2025). Following prior work, we concatenate the N input images each containing H x W tokens tokens-along the token dimension and jointly predict all views Or.t+HH In experiments, we find multi-view joint prediction also improves consistency and substantially reduces hallucinations.
Pose-conditioned Memory Retrieval Mechanism. Prediction errors in world models tend to accumulate over long rollouts, leading to drift and incoherence. To mitigate this, we augment the model input with past frames. To prevent the context from becoming too long, sample & history
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Pr-kes
Oz-2m Oc-mx
02
Timeline
P NxixxW
Sparse History
Noised Future
Frame-Level Pross-Attention
[0-- Exe Op-at Ørl
[
Spatial Tokens Action Chunk
(BxT P.
(BXT,P.C)
Spatial Transformer
(,
(BxP.T.C)
(#XT,
CLIP Samantic Takens
Temporal Transformer
14c-Aw ... -..4- 4cl
(8. C2)
H: Lotent Height
W: Latent Width
Figure Ctrl-World is initialized from a pretrained video diffusion model and adapted into controllable, temporally consistent world model with: (1) Multi-view input and joint prediction for unified information understanding. (2) Memory retrieval mechanism, which adds sparse history frames in context and project pose information into each frame via frame-level cross-attention, re-anchoring predictions to similar past states. (3) Frame-level action conditioning to better align high-frequency action with visual dynamics.
frames with a stride m, enabling the model to predict 0++++ ~ --.
Additionally, embed the corresponding robot poses [q: (qtkmm -... Qt) into frames (0--km .... 0i] via frame- wise cross-attention within spatial transformer. This allows the model to use the arm pose to identify relevant frames from the past, effectively anchoring future predictions relevant history.
Frame-level Action Conditioning, The pretrained video model conditions only on text and image, which limits its control precision. To enable full controllability, additionally condition the model on the action sequence [a++1=+H]] output by the policy. We also transform each action sequence into Cartesian-space -ppsie robot arm poses [ + and concatenate with past poses [gt-km .... Qt-ms qe]. Frame-wiss cross-attentinn
(Zhu et al., 2024, He et al. 2025) is then applied within the spatial transformer, allowing the visual tokens of each frame to attend to its associated pose embedding. For
history frames, this pose corresponds to [4: (4t-km, ... Qt-m, yr). while for future frames, corresponds to [d/+1:++1l-
Training Objective. We initialize our model with the pretrained 1.5B Stable- Video-Diffusioo (SVD) model (Blattmann et al. 2023a). To inherit the knowledge and structure in the pretrained vidco model, only newly initialize an action- pirtonn-roeetiion MLP for the input actions and keep other parameters unchanged at initialization. Then this action-conditioned world model is fine-tuned with diffusion loss (Ho al. 2020; Karras et al. 2022). During training, the prediction target 2g O1+1:++ is perturbed with Gaussian noise c ~
(0, 1) at diffusion step t' E [0, T' with scheduler Tæ. resulting in Iep Varto + vI apter The model input is the concatenation of history tokens and the noised future: [O: [Ot-kmy ... Ot- my Ot, J. The overall training objective is:
C = = ( (t, - xoll
where án denotes the model's prediction, and e = [ge- [ It, // O kmy 0.] corre- sponds all model inputs. More details of the model can be found in the Appendix A
4.2 USING WORLD FOR POLICY EVALUATION AND IMPROVEMENT
Policy Evaluation within World Model. Once controllable and consistent world model is trained, we can conduct policy-in-the-lowe rollouts in imagination space. Given an initial observation do and instruction a policy together with the world model W can generate synthetic trajectory The initial observation can be sampled from the validation dataset or recorded as a snapshot from a real-world setup. In experiments, preference judgments. While recent works (Du et al. 2023) explore the use Vision-Language Models general- purposs reward models, leave such extensions to future work.
label each trajectory
success failure based human
Policy Improvement with Synthetic Data. Beyond evaluation, the world model enables searching for successful synthetic trajectories to improve policy performance. We observe that, under fixed
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Algorithm 1 World Model Rollout and Policy Improvement
Given: policy Tæ, action perturbation function 6a. world model W, task instructions [1", ,,, [M with initial obs [ o synthetic dataset D., interaction step N, action horizon H.
1: for = 0 to .M do
2:
[oo]
3:
for j to N do
4:
Current observation: o = r(t) where t j=
5:
Sample action from perturbed policy: G1+1:+41 = no (o,
> For diverse rollouts
6:
Prepare history context: h [0t-Am; Ot- 0m22 Ot-m
7:
Make predictions with world model: 0++1:++ = Wh,, Ot, 0++1:4+1)
8: Add predictions into trajectory: T TT O++1:++H.
9: end for
Judge success of T based on human- prennan-ereerence.. Add 7 into g success.
11: end for
12: Finetune my with Ly Ben.oessn=-D.llss
initial observations and instructions, policy behavior tends to be highly deterministic. For example, the policy tends to grasp the same object across multiple trials, rather than stochastically reaching for various objects. To explore larger search space, we introduce structured perturbations to encourage diversity in rollouts. Specifically, we can (i) rephrase the instructions, since VLA policies tend to be steerable, exhibiting different behaviors response different instructions; or (ii) reset the policy to random initial states within the world model, which leads to diverse initial observations. Starting from a set downstream tasks with language instructions [ [d], we collect synthetic rollouts and score them based on human preference. To improve the policy performance, we fine-tune the
policy on successful trajectories. The overall procedure summarized in Algorithm
5 EXPERIMENTS
In this section, conduct experiments evaluate Ctrl-World. We aim to answer the following questions: (1) Can Ctrl-World generate long-horizon rollouts that are both spatially and temporally consistent, while maintaining high controllability? (2) Can Ctrl-World reliably evaluate different generalist robot policies in imagination space, faithfully reproducing their real-world performance rankings? (3) Can Ctrl-World improve a policy's 's instruction following by discovering and synthesiz- ing successful trajectories entirely within its imagination?
5.1 EXPERIMENT SETUPS
DROID Platform and Dataset. Our experiments use the DROID platform (Khazatsky et al. 2024), which features Panda robot arm equipped with a Robotiq Gripper. The platform includes one wrist-view camera and two randomly positioned third- view cameras that observe the workspace. The DROID dataset (Khazatsky et al. (2024) contains 95,599 diverse trajectories collected from 564 scenes, providing dense coverage of the workspace. This includes about 76k successful and about 19k failed trajectories. The inclusion of diverse actions and failure data is crucial, as it allows us to train a controllable world model that can simulate a wide range of future scenarios.
Training Details. During training, our model jointly predicts outputs from all three cameras, each with resolution of 192x320. The model is conditioned on history of frames, with an interval of 1-2 seconds between frames. We condition the model on the next 15 future actions, which corresponds one second action chunk DROID. During interaction, if policy's output is less than 15 steps, we pad the action chunk with dummy actions and only use the predictions for valid actions. We train the model on 2x8 8 H100 GPUs, with a total batch size of 64. Training takes approximately 2-3 days.
5.2 WORLD MODEL QUALITY ANALYSIS
Baselines and Evaluation Matrices. We quantitatively compare our model, HallWorld, against two prior action-conditioned world models: World-model-basee Policy Evaluation (WPE) (Quevedo et al.
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Computation- based
Model-based
Evaluated Camera
Method
PSNR
SSIM
LPIPS
FID
FVD !
WPE--ingle- View
20.33
0.772
0.131
25.50
156.4
WPE-Multiview
21.17
147.1
Third-view Camera
SiAA-Siiiiigee- View
21.36
0.774
0.117
26.46
138.1
TRASim- -Sii--uultviiw
20.21
165.4
Ctrl-World-Singg Www.o-rrrri----eeee---- View
21.27
0,793
0.110
23.47
127.5
Ctrl- forlWorld (ours)
23.56
0.828
0.091
25.00
97.4
Table I: Quantitative results for interactive long-trajectory generation on the validation set. We evaluate our world model's quality by generating 10-second trajectories. Given a randomly sampled initial frame, the model receives a 15-step action chunk (spanning over second) in each interaction and generates for 10 rounds auto-regressively. The results are averaged over 256 clips.
Ground Truth
IRASim
Ours
Initial Obs
Initial Obs
Figure 3: Qualitative results on long-horizon horizon rollouts from the validation set. Prior models rely on single-view prediction, suffering from partial observability and hallucinations (e.g.. failing to move the green towel grasp the red bowl). In contrast, Ctrl-World jointly predicts from third-view and wrist-view cameras, yielding precise future trajectories aligned with the ground truth.
Evaluated
Computation-hased
Model-based
Method
Camera
PSKR
SSIM
LPIPS
FID
FVD !
Ctrl-World
23.56
0.828
0.091
25.00
97.4
Third-view
Ctrl-World w/o memory
23.06
0.812
0.099
26.14
105.5
Camera
Ctrl-World w/o frame-leve cond
21.20
0.789
0.109
27.52
122.7
Ctrl-World
19.18
0.665
0.252
25.78
127.1
Wrist-view
Ctrl-World w/o memory
18.84
0.655
0.265
26.23
133.1
Camera
Ctrl-World w/o frame-level cond
15.69
0.571
0.375
33.51
179.1
Ctrl- World w/o joint pred
15.94
0,580
0.345
26.46
158.1
Table 2: Ablations on key components in Ctrl-World. Removing memory mechanisms, frame-level action conditioning or multi-view joint predictions all lead to performance drop.
2025) and IRASim (Zhu camera view, we train a single-view version, Ctr-World-third-vieew single third-person camera for fair comparison. For evaluation, which only inputs and predicts on as a validation set and randomly sample 256 video clips, each 10 in length. During rollouts, the world model receives 15-step action chunks (corresponding to s) and autoregressively predicts the next frames for 10 steps, producing 10 s-long trajectories. We then compare the predicted videos against ground truth using both computational (PSNR (Hore Ziou 2010) and SSIM (Wang et al.) 2004)) and model-based metrics (LPIPS (Zhang et al. 2018), FID (Heusel et al. 2017), and FVD (Unterthiner et al. 2018))
2024). Since these models only predict from a single third-person
hold out 2% of the trajectories
Quantitative and Qualitative Results on Multi-step Interaction Trajectories. As shown in Table I Ctrl- woorld-hhird-vie outperforms these prior models, and multi-view joint prediction further improves generation quality. Consistent with observations from prior work (Quevedo al. 2025 Zhu et al. (024)), we also find that these baselines struggle to capture robot-object interactions and often generate hallucinated predictions. For instance, shown in Figure 3 single-view prediction

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T=Os
T=1s
T=2s
T=3s
T=45
Close
-6 cm
-6 cm
Gripper
v6 cm
-3
-2 cm
-10 cm
Gripper
15"
15
15"
6 cm
Attention Score
0.3
axis
axis
A axis v6
cm
-6
+6
axts
cais
X axis
X axis
-6
-6
+6
06
35
Withhott memory
X axis
X axis
oxis
X mxis
Wiithott Frame-level
-6
Pose Condition
Figure 4: Controllability of Ctrl- World and ablations. Different action sequences can produce distinct rollouts in Ctrl-World with centimeter-level precision. Removing memory leads to blurry predictions (blue), while removing frame-level pose conditioning reduces control precision (purple). Attention visualization (left) when predicting the 4 frame shows strong attention the f 0 s frame with the same pose, illustrating the effectiveness of memory retrieval. For clarity, each action chunk expressed in natural language (e.g., "Z-axis -6 cm"). Due space constraints, only the wrist-view is visualized for intermediate frames.
t=0
t= 3s
t 6s
t= 8s
[ 9s
t 10s
Wrist-view Ground Truth
Figure 5: Consistency of Ctrl-World. Since the wrist camera's ' field of view changes dramatically within a single trajectory, leveraging multi-view information and memory retrieval essential for generating consistent wrist-view predictions. Prediction highlighted in the green box are inferred from other camera views, while those in the red box are retrieved from memory.
all fail In contrast, Ctrl- World precisely models the bobot-object interactions through joint prediction of the wrist-camera view, which provides critical, fine- aie-ggrined information about contact events and object state changes.
methods WPE, IRASim and Strl-Wordd-third-iee
the green towel the red bowl.
Controllability of the World Model. A key requirement of a world model the ability simulate diverse future outcomes conditioned on different actions. We find that our model exhibits fine- grained controllability, producing precise future predictions even for actions that differ by only few centimeters (see Figure 4.. We hypothesize that this controllability arises from two main factors: first, the dense action space coverage in the DROID dataset; and second, our use of multi-view prediction and frame-level action conditioning, which is also supported by our ablation studies. On the left side of Figure 4 we visualize the attention weights when predicting the t = 4 s frame and observe strong attention to the f = Os frame with similar pose, highlighting the effectiveness of memory retrieval mechanism.
Consistency of the World Model. For the wrist camera, since the camera's 's field of view changes dramatically within a single trajectory. is challenging for models to generate consistent, long-term predictions. As shown in Figure 5 find that model effectively leverages relevant information
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from both other camera views and historical frames, enabling it generate consistent wrist-view predictions. Ablations on memory components and frame-level conditions are Table which confirm the importance of each component.
5.3 WORLD MODEL FOR POLICY EVALUATION
In this section, we evaluate whether Ctrl- World can be used to evaluate the instruction-following following ability generalist robot policies and accurately reflect their performance rankings in the real world (L.i et al., 2024). We set up our own DROID platform and randomly place two third-person cameras around the workspace. Similar to how prior works have seen DROID policies generalize to setups (Pertsch al. 2025), find that Ctrl- World, pretrained solely the open-sourced DROID dataset, can make accurate future predictions zero-shot in our newly configured scene with novel camera placements.
Policies and Tasks, We evaluate three publicly released policies, To (Black et al. 2023), To- FAST (Pertsch et al., 2025), and Tu.5 (Intelligence al., 2025), across diverse tasks including Pick-and-Place, Towel-Folding
Drawer, Wipe-TTable, Close-Laptop Pull-tissue and Stack tasks DROID platform. We initialize real-world and world model rollouts with the observations and exccute each policy, following Algorithm|! [
initial
We report instruction following rates and success rates in Figure and visualize qualitative comparisons between real and imagined rollouts in Figure 6 More rollout details can be found in Appendix
Initial Obs
*Kg.5 World
fens Rollout
"Place blue block on the plate."
"Fold the Towel."
"Pull one tissue out of box""
"Wipe table from left to right."
laptop."
Figure 6: Comparisons between mo.5 rollouts in the real-world and world model. Each trajectory contains 20 interactions between To.s and Ctrl-World Remarkably, both the generalist policy and Ctrl-World transfer shoo to our new DROID setup.
Tasks
Generalist Policy
Pick-Place
In World Modal
In World Modal
Fold-Towel
Pio-fast
Drawer
0.87--0.04
0.81x
Pio5
Wipe-table
Regression
Close-laptop
Oracle
Pull-tissue
Stack
0.8
0.8
Instruction
Following Real World
Rate in Real World
Figure 7: Quantitative correlations between real-world and world-model rollouts. The world model reliably captures
behavior but tends underestimate the execution success rate.
Comparison Between Real- World and World Model Rollouts. Our results show that policy's high-level cestruction-oolloiinn behavior in the world model is closely correlated with that observed in the real world. However, notice some gaps in evaluating low-level execution, specifically in
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precise modeling of complex physics dynamics such as collisions, objects sliding away, rotations, etc. (e.g., interaction with laptop is imprecise in Figure 6). We also observe that generalist policies tend to keep retrying in the real world after failed attempts, which the world model sometimes does not capture. Although some failure trajectories are included in the DROID dataset, there are still many failure modes outside the data distribution. We expect that collecting additional in-domain policy rollout data would improve the fidelity of the learned dynamics and narrow this gap (Team 2025
5.4 WORLD MODEL FOR POLICY IMPROVEMENT
Post-frain Policy with Synthetic Data. We now evaluate whether Ctrl-World can be used to generate synthetic post-training data for improving VLA models without real-world data. We use To.5 as our base policy and follow Algorithm [ As described in Section 4.2 we encourage rollout diversity by either (1) rephrasing task instructions or (2) resetting the robot arm to a new initial state. For rephrasing, call LLM API (Team et al. 2023) to paraphrase instructions (e.g., transforming "place glove in box" into "pick up the cloth and put it inside the box"). For resetting, we randomly sample a new target initial position and move the robot planner before policy-interaction begins. We generate 400 trajectories per task and retain 25-50 successful trajectories based human preference judgments. This selection step could be automated with reward models, which is an active area of research (Ma, 2025). Finally, we fine-tune the policy on the curated synthetic dataset for 2k steps, improving base model 's capability in unfamiliar instructions and objects.
there using linear-nterpolation motion
Spatial Understanding
Direetinn
Shape Understanding
Object
Pick the top loft Aeft.
Pick the
Pick lorger green object.
Pick place
Pick place box.
right
from right.
Figure 8: The top row illustrates examples of post-training tasks, while the bottom row presents synthetic trajectories generated within the world model. The world model can produce both successful and failed rollouts; we keep the successful trajectories and use them for policy fine-tuning.
Results. Some representative task examples and syn- thetic trajectories visualized in Figure g and quan- titative results are reported in Figure While the pretrained To.5 policy achieves low success rates unfamiliar objects and novel instructions, post-training aligns the model with new instructions and boosts the success rate from 38.7% to 83.4% on these downstream tasks. We include task details in Appendix
Dase Policy
finetured Pollcy
0.20
0.151
0.21
0.2
0.0
Spatial
Shape
Taweel-Dir
Nevel-Otj
Averupe
Policy improvement. Post- training on synthetic data improves policy folluciion-following by 44.7% on average.
Figure 9:
CONCLUSION
We presented Ctrl-World, controllable world model
for robot manipulation that supports closed-loop policy evaluation and improvement entirely within the model's imagination. Policies evaluated in Strl-World exhibit instruction-follee following behaviors that closely mirror those in the real world. Notably, post-training on generated data boosts the pretrained robot policy's success rate novel instructions from 38.7 7% to 83.4%.
Despite these promising results, important challenges remain. Our model can fail on tasks involving precise interactions or long- honghhorizon reasoning, and performance is sensitive to initial observations.

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Published as a conference paper at ICLR 2026
These limitations may diminish video backbones become more physically accurate and coherent over time (Ball et al. 2025, Agarwal et al. 2025). In addition, our experiments focus on improving instruction tollowing. and expect that our model is not accurate enough to improve performance in other aspects such as the low-level success rate on previously seen instructions. Improving the model with iterative policy roll-out and fine-tuning is an exciting future direction. Looking forward, we believe generative world models can transform how robots acquire new skills, enabling scalable policy evaluation and allowing them to learn not just from real world experience, but also safely and efficiently from generated experience.
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Published as a conference paper at ICLR 2026
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Code can be found in the Supplementary Materials.
More videos can be found at the anonymous website: bttps://sitee http::///iittss.oooleeeeo//o//// -wordd
A
MORE DETAILS FOR WORLD MODEL LEARNING
Model Architecture. Our world model closely follows the architecture of Stable Video Diffusion (SVD) [Blattmann et al. 2023aj, and initializes from the SVD pretrained checkpoint. The only newly initialized component is 3-layer MLP that projects 7-dimensional Cartesian-space actions into 1024-dimensional latent embedding.
The input images are first encoded by VAE with spatial downsampling ratio of 8 x 8. In practice, we use k = 7 history frames, each perturbed with independent random noise to improve robustness. We set the action conditioning window be second, corresponding 15 action steps. To reduce GPU memory consumption, we transform these 15 actions in the Cartesian space (see Section B and temporally downsample them steps before feeding them into the model.
Each frame contains three 192 x 320 images, which are encoded into latent features of shape 24 x 40. The resulting total input token shape is (7 + 5) x 24 40), which is then processed by the spatial-temporal transformer backbone.
Training Datasets. We use all 95k trajectories from the DROID dataset. For each training step, we randomly sample a trajectory and then uniformly sample a frame within that trajectory the current frame. We then retrieve memory frames by sampling backward in time and set the model's prediction target to be the subsequent future frames.
Training Process. We train the model on 2x8 H100 GPUs with total batch size of 64. The learning rate is set to be le-5, and we train for 100k steps, which takes approximately 2-3 days to complete.
B MORE DETAILS FOR POLICY EVALUATION
Details on interaction between policy and world model. We directly use the offi- cial DD-DRDD FA-S--DDDO,,,, and -D.SD policies from https://gi to interact with Ctrl-World, To the best of our knowledge, Ctrl-World is the first world model that enables policy-in-the-lown art VLA model. These open-sourced policies take joint angles and two views of camera input and output joint velocities. contrast, our world model conditions on the end-effector pose in Cartesian space. To bridge this mismatch, train adapter on the DROID dataset that maps the current joint angles q. and predicted joint velocities d 1:44 into future joint configurations joint 1--1:
com/
Physical- www.--nneeiiggnne/ppppeee
interactions between statc-of-the-
We then apply Franka Panda forward kinematics (FK) to convert these joint configurations into Cartesian- Sspesi poses caressin The adapter is implemented as a simple two-layer MLP.
The overall process is follows: given the current joint configuration g. multi-view observation Ot, and language instruction 1, the policy outputs H-step joint velocities:
jv d411+
((g. oo. I).
These are passed through the adapter to predict future joint configurations, followed by FK to compute Cartesian poses:
44144 = Adapter(q. joint .v
joint
af+14+H)
cartesian
1:t+ = K F (4441444)
Finally, the world model predicts the next H/ frames conditioned on the current observation, the calculated Cartesian poses, and the history Cartesian poses:
0++1+ W (M 11+1:t-
Thistory
This setup enables fully autoregressive rollouts, allowing the official कo DROID, Fn FAST-DROID, and To.S-DROID policies and Ctrl-World interact seamlessly in imagination space.
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Task
Method
Instruction Following
Success Rate
Real world
World Model
Real world
World Model
Pick-Place
T0
0.75
0.60
0.70
0.55
To-fast
0.90
0.75
0,85
0.70
WO.5
0.90
0,80
0.85
0.70
Fold-Towe
To
0.40
0.20
0,40
0.20
To-fast
0.55
0.45
0.55
0,40
Wo.5
0.80
0.75
0.75
0.65
Drawer
0.05
0.00
0.05
0.00
To-fat
0.20
0.20
0.15
0.05
No.5
0.80
0.65
0.80
0.30
Wipe-table
To
0.20
0.10
0,20
0.00
To-fast
0.50
0.35
0,50
0.35
To.5
0.80
0.70
0.80
0.65
Close-laptop
0.15
0.20
0.15
0.00
To-fast
0.60
0.50
0.50
0.00
To.5
0.80
0.70
0.70
0.05
Pull-tissue
To
0.30
0.20
0.30
0.20
fast
0.10
0.0
0.10
0.0
To.5
0.85
0.60
0.75
0.50
Stack
To
0.75
0.45
0.60
0.40
To-fast
0.80
0.70
0.70
0.55
To.5
0.80
0.65
0.65
0.55
Table 3: Comparison of instruction-following and success rate across methods and tasks.
Breakdown for policy evaluation. We present the instruction-follene followiin and low-level execution success rates in T
Task details and criterion. our experiments, we use human annotators to evaluate whether each trajectory is success or failure. Although this evaluation process can be automated in the future using large vision-language reward models, our focus this paper is on the world model itself, so we rely on human preference as the reward signal. We provide clear criteria to determine whether trajectory merely follows the instruction or achieves full task success:
Pick-place: Several objects and receptacles placed the tabletop. The instruction is of the form "Pick up A and place in B." A trajectory is considered to follow the instruction the policy attempts to grasp the correct object It is considered a success if object A is successfully placed into the target receptacle B.
towel is lying flat on the table, with other objects possibly present. The instruction is "Fold the towel." A trajectory is considered to follow the instruction if the gripper moves to the towel's edge and attempts to lift and fold it. trajectory considered successful if the towel's surface area becomes half in the end.
Fold the Towel:
The instruction is to "Place object A into drawer". trajectory follows the instruction if the robot attempts place object A inside the drawer. It is success if object A is eventually placed the drawer.
Drawer:
Wipe Table:
The instruction is wipe the table surface. trajectory follows the instruction if the gnipper makes contact with the towel and moves in sweeping motion. It is considered successful if large portion of the table covered by the sweeping motion.
Close Laptop: The instruction is to close an open laptop. A trajectory follows the instruction if the gripper approaches the laptop lid. considered successful if the lid is fully closed.
The instruction is pull tissue from tissue box. A trajectory follows the instruction if the gripper approaches the tissue slot and pinches a tissue. is considered successful if at least one tissue is fully extracted.
Pull Tissue:
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Published as a conference paper at ICLR 2026
The instruction is stack object top of another. A trajectory follows the instruction the gripper lifts the correct object. It is success the object is placed stably top of the target object.
Stack:
C
MORE DETAILS FOR POLICY IMPROVEMENT
Finetuning Process. We finetune xn.5 DROID policy based official codebase https://
github. www.cc/mccc/.eeee iissiiaaln cariicllnnnnee wew.eegggee///eeeeeeeeeeo openpi / synthetie dataset for 2k steps 4 H100 GPUs.
We finetune the pretrained checkpoint
Task Descriptions:
Spatial Understanding Tasks: 2-6 random objects are placed on the table. The policy is instructed to pick an object at specified spatial location and place it in the box. Example instructions include: "Pick the object on the top-right side and place it in the box" or "Place the object on the far-left side into the box."
Shape Understanding Tasks: 2-3 random objects are placed on the table, where some share the same attributes but differ in size. The policy must distinguish objects based on the size. Example instruction: "Pick the larger red block and place it in the box."
Towel-Foldine with Directions: A towel and other distractor are placed on the table, and the policy is given instructions specifying particular folding direction (e.g.. "Fold the towel from left to right").
Novel Objects: We introduce unseen objects such as a glove and a stapler which Pretrained policy can not identify very well.
Detailed success rate. We provide detailed task success rates inside each categories:
Left
Right
Bottom
Top
Lefi Top
Left Bottom
Right Top
Right Bottom
Average
Base Policy
0.50
0.45
0.30
0.45
0.15
0.20
0.05
0.20
0.2875
After Post- Post- Traininin
0.85
0.90
1.00
0.80
0,90
0.875
Table 4: Policy improvement (Spatial Understanding).
Big Left
Big Right
Small Left
Small Right
Average
Base Policy
0.40
0.45
0.40 0.95
0.50
0.4374
After Post-Training
0.85
0.95
0.90
0.9125
Table Policy improvement (Shape understanding).
Towel-1
Towel-2
Towel-3
Towel-4
Average
Base Policy
0.60
0.50
0.55
0.65
0.575
After Post- fost-Training
0.75
0.8
0.85
0.80
0.80
Table 6: Policy improvement (Towel folding with direction).
Novel-obj-glove
Novel-obj-stapler
Average
Base Policy
0.20
0.30
0.25
After Post-Training
0.80
0.70
0.75
Table 7: Policy improvement (Novel object).
18


Reviews:
[{"review_id": "jmxVYg9Sj2", "reviewer": "Reviewer_qxWr", "summary": "This paper introduces a controllable world model designed for policy-in-the-loop learning, capable of generating multi-view, spatially, and temporally consistent rollouts conditioned on past robot states, observations, and future actions. The model is trained on the DROID dataset and validated in real-robot experiments. The proposed approach reportedly achieves substantial improvements in policy success rate through trajectory synthesis for supervised fine-tuning. Overall I think this is a solid paper with novel idea that using world-model-based data synthesis for policy fine-tuning, which holds strong potential for advancing applications of world models.", "questions": "1. It will be great to see failure cases for each experiments on the website.\n2. Are the objects in the experiments ID or OOD? How does it generate to very novel objects?\n3. What is the inference time?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. The idea of controllable world model with correct action conditioning and cross-frame memory retrieval is important for practical world model application, this paper provides a straightforward yet effective solution towards this goal.\n2. The presentation of this paper is good. Sections are well-organized, results are delivered clearly with figures and plots. Illustrations are well-made and helpful.\n3. The proposed world model can be taken as a \"plug-in\" strategy for various pre-trained policies, which may have wide applications.\n4. The performance of the proposed world model looks great, the frame-level pose condition and memory retrieval mechanism seems effective.\n5. The training cost of this world model is relatively small.", "weaknesses": "1. The proposed world model purely focus on the robot motion and takes interaction with other objects in the scene as environmental dynamics, which may straggle with tasks that involves multi objects or complex interactions.\n2. Based on the videos, there are still gaps between real rollout and imagined rollout, especially when the end-effector interacts with objects.\n3. The demonstrated tasks are still limited to \"pick-and-place\" style motions, it would be great to see failure cases on more complex robot tasks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "gXECQGCRWp", "reviewer": "Reviewer_gAW8", "summary": "The paper proposes CTRL-WORLD, a controllable, multi-view video-diffusion world model for robot manipulation designed to run policy-in-the-loop. Key components are: (i) joint multi-view prediction (including wrist views), (ii) frame-level action conditioning, and (iii) a pose-conditioned memory retrieval mechanism. The model is initialized from Stable Video Diffusion, fine-tuned on 95k trajectories, 564 scenes, and interacts with $\\pi$0/$\\pi$0-FAST/$\\pi$0.5 policies via an adapter that maps joint-velocity actions to Cartesian poses. The authors report better video metrics than prior single-view world models, correlation between imagination-space and real-world rollouts, and a +44.7% improvement in instruction following for $\\pi$0.5 after fine-tuning on curated synthetic rollouts.", "questions": "1. What happens if CTRL-WORLD conditions directly on joint-space action chunks, i.e., with or without poses? Please provide adapter error statistics and an end-to-end ablation isolating its effect on Fig. 7 correlations.\n\n2. Can you extend IRASim/WPE to multi-view and re-run Table 1 to confirm that the gains are not primarily from adding wrist view rather than the proposed mechanisms?\n\n3. Provide results varying rollout horizon (10–30 s) and memory parameters (k, m), showing how PSNR/FVD and instruction-following correlation degrade with horizon.\n\n4. Please include size-matched controls: (a) random uncurated imaginations, (b) paraphrase-only vs. reset-only vs. both, (c) selection via a reward-model instead of humans, and (d) real DROID successes of equal count.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper cleanly adapts a large video diffusion backbone into an action-conditioned, multi-view world model with explicit mechanisms for long-horizon consistency and control. The system is carefully described, including history frames, frame-wise cross-attention with poses/actions, and the memory stride idea.\n\n2. The adapter that converts joint-velocity chunks into future Cartesian poses enables closed-loop interaction with modern VLA policies, which is practically useful.\n\n3. Ablations show significant drops on both computation-based and model-based video metrics. Quantitative tables and qualitative rollouts are informative.\n\n4. The paper demonstrates a concrete synthetic-data workflow, i.e., instruction paraphrasing with initial-state resets, as well as human-preference curation, that improves instruction following of $\\pi$0.5 on downstream tasks.", "weaknesses": "1. Both the evaluation labels and the selection of synthetic trajectories rely on human preference judgments; the protocol, rater agreement, and quality controls are unspecified. Because only successful imaginations are kept for fine-tuning, there is a risk of optimistic bias in the reported improvement, especially absent size-matched controls versus random or uncurated synthetic data. \n\n2. Policies act in joint-velocity space, whereas CTRL-WORLD conditions on Cartesian poses via an MLP + FK adapter. There is no error analysis or ablation isolating the adapter’s contribution. Given that several physics errors (e.g., collisions/sliding) are noted, the adapter may be a major bottleneck that is not rigorously evaluated.\n\n3. The multi-view token concatenation, frame-level conditioning, and memory retrieval are each known ideas. The paper’s novelty is mainly their combination and scale on DROID. Stronger baselines, e.g., extending IRASim/WPE to multi-view, and theoretical insights are absent, the contribution feels incremental. \n\n4. The text highlights generalization for “over 20 s”, but most quantitative evaluation reports 10 s trajectories. The horizon sensitivity is not analyzed, e.g., the memory stride/length hyperparameters.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "kHJJPcV4eB", "reviewer": "Reviewer_bxDF", "summary": "This paper proposes a method for building a multi-view world model that supports fine-grained action control and stable long-horizon generation. The authors conduct extensive experiments to verify the effectiveness of Ctrl-World in visual generation compared with baselines, and perform ablation studies to demonstrate the effectiveness of the three proposed techniques. In addition, the authors carry out policy evaluation and synthetic data experiments to validate the effectiveness of Ctrl-World for robot learning.", "questions": "1. Some papers [1][2] discuss the issue of visual degradation, and their solution is to add small amounts of noise to the conditioning frames to mitigate distribution shift. What is the fundamental difference between that approach and the Pose-conditioned Memory Retrieval Mechanism proposed in this paper? Which method is more effective for addressing visual degradation?\n\n2. The main difference from other action-conditioned world models is the addition of language instructions. This means video generation is jointly constrained by both the text and the action chunks. In this setting, what is the role of the text input? I am curious about the motivation for this design and whether there is strong evidence supporting its necessity.\n\n[1] IRASim: A Fine-Grained World Model for Robot Manipulation. ICCV 2025.\n\n[2] Training Agents Inside of Scalable World Models. Arxiv.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. This paper addresses a practical problem and demonstrates the effectiveness of wrist-mounted cameras for robot world models.\n2. The authors conduct detailed real-world experiments to verify the effectiveness of using world models for policy evaluation.\n3. The synthetic data experiments help expand robotic arm datasets, and the experimental results in the paper show strong potential.", "weaknesses": "1. The technical novelty is relatively weak. Adding a wrist camera for joint prediction is not very challenging, and similar multi-view world model ideas already exist in autonomous driving, but are not discussed here [1].\n2. The idea of Frame-level Action Conditioning (lines #185–#191) has already been used in [2][3], but here it is not properly cited or explained, which may confuse the source of the contribution.\n3. Some doubts about Section 5.3: policy evaluation should test the same architecture with different checkpoints or slightly different training strategies. The world model should be able to reflect these differences; only then can it replace the real world to evaluate policies during development, instead of evaluating completely different architectures. Of course, testing on more architectures is good, but the key is whether the world model can detect subtle differences in success rates, similar to [4].\n4. Some doubts about Section 5.4: the source of performance improvement is unclear — is it from rewritten instructions or from the world model? My main concern is reproducibility and real validity. This experiment is very similar to [5], but lacks detailed discussion and comparison.\n\n[1] Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models. Arxiv.\n\n[2] IRASim: A Fine-Grained World Model for Robot Manipulation. ICCV 2025.\n\n[3] Pre-trained video generative models as world simulators. Arxiv.\n\n[4] SimplerEnv: Simulated Manipulation Policy Evaluation Environments for Real Robot Setups. CoRL 2024.\n\n[5] DREAMGEN: Unlocking Generalization in Robot Learning through Video World Models. CoRL 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "qt55h28rtn", "reviewer": "Reviewer_strQ", "summary": "This paper proposes Ctrl-World, a controllable multi-view generative world model for robotic manipulation that enables policy-in-the-loop imagination rollouts for both policy evaluation and improvement. By integrating multi-view joint prediction, frame-level action conditioning, and pose-conditioned memory retrieval, the model achieves spatially consistent, controllable, and long-horizon video generation. Trained on the large-scale DROID dataset, Ctrl-World can simulate policy behavior across diverse camera views and environments, achieving close alignment with real-world performance and improving policy success rates by 44.7% through synthetic fine-tuning. Overall, the work offers a significant step toward scalable simulation-based evaluation and data-efficient improvement of generalist robot policies.", "questions": "This paper presents an interesting exploration of action-conditioned embodied video generation. My main questions are:\n\n- How does the method differ from existing approaches such as EVAC, which also employ multi-view and sparse-memory designs?\n\n- Can the model accurately generate videos for complex cases like deformable or articulated objects, given its lightweight diffusion architecture?\n\n- Can the generated data be directly used for policy training, or can the model serve as a video-based simulator for scalable RL?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- This paper explores an interesting and important problem in the field of robotic manipulation, action-conditioned embodied video generation. Leveraging video generative models to simulate action policies represents a promising research direction, as it enables more efficient and scalable simulation, potentially facilitating the future development of RL in VLA systems.\n\n- The proposed pipeline is well-designed and comprehensive. Through the incorporation of memory mechanisms, multi-view representations, and action-conditioning, it effectively ensures controllability between the generated videos and the corresponding actions.\n\n- The paper also provides extensive real-world experiments. The authors validate the effectiveness of the proposed Ctrl-World framework in simulating policy models across different robotic platforms.", "weaknesses": "- The paper addresses an important problem in action-conditioned embodied video generation for robotic manipulation. However, I notice that similar approaches, such as EVAC [1], have also explored multi-view representations and sparse memory mechanisms. The paper would benefit from a more detailed discussion and comparison with such related work.\n\n- Another concern lies in the simulation fidelity of object dynamics. For computational efficiency, the proposed method employs a relatively compact video diffusion network. It remains unclear whether the generated videos are sufficiently accurate for downstream simulation purposes—especially for deformable objects, where precise modeling of shape deformation is essential, and for articulated objects, where accurate representation of hinge or rotational motion is critical.\n\n- Finally, the practical applicability of the proposed model requires further clarification. It would be valuable to discuss whether the generated data can be directly used for policy training or whether the model itself could function as a video-based simulator to enable scalable reinforcement learning. This aspect is crucial for assessing the long-term impact of the approach, but it is not thoroughly validated in the current version.\n\n- A current limitation is that all demonstrated examples and evaluations focus exclusively on successful policy trajectories, lacking simulation or analysis of failure rollouts. This prevents verification of whether the generated videos truly reflect accurate action–visual mappings. The paper should further compare and analyze cases where, under identical initial states, both successful and failed action policies are executed to assess the model’s fidelity and controllability.\n\n[1] EnerVerse-AC: Envisioning Embodied Environments with Action Condition. arxiv:2505", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)