ConfAL-WM: Confidence-Guided Active Learning for Action-Conditioned World Models

Xiang Liu; Kunwei Wu; Miao Liu; Sen Cui; Changshui Zhang

Figure 7: Training and inference of the confidence probe in the UNet latent diffusion world model. The figure follows the same training and inference pipeline as Figure 2 .

Figure 8: Patch-level latent error construction. Channel-wise latent prediction errors are first averaged into a spatial error map and then pooled over the region aligned with each confidence-probe patch.

Figure 9: Dynamics of the EMA-based adaptive threshold band. The probe uses a stochastic threshold

\theta\sim\mathcal{U}(l^{(s)},h^{(s)})

, where

l^{(s)}

and

h^{(s)}

are the EMA-tracked lower and upper bounds at training step

s

, and the black curve shows their midpoint

(l^{(s)}+h^{(s)})/2

. (a) During the first 100 steps, both bounds decrease rapidly and quickly approach a stable range, reflecting fast adaptation to the current error scale. (b) Over a longer set of sampled training steps, the threshold band shows a clear transition from a fast-changing warmup regime to a slower and more stable regime.

Figure 10: Task-level risk aggregation in latent and pixel spaces. Mean, tail, and persistent risk scores are compared with the mean oracle-error statistics over the 50 prescreened tasks. Dashed lines show linear fits, shaded regions denote confidence intervals, and each panel reports the Spearman correlation. Mean aggregation gives the strongest latent-space correlation, whereas tail and persistent aggregation emphasize different failure patterns.

Figure 11: Spatial agreement between high-risk and high-error patches. Top-

k

IoU and overlap@

k

are reported for the top-

1\%

,

5\%

,

10\%

, and

20\%

patches in (a) latent and (b) pixel spaces. The increasing scores at larger ratios reflect coarser spatial coverage and should not be interpreted as improved localization resolution.

Figure 12: ECE and Brier scores under different operational thresholds. Calibration is evaluated by sweeping the correctness threshold

\theta_{\mathrm{eval}}

in (a) latent space and (b) pixel space. The confidence maps are fixed throughout; only the binary correctness definition changes.

Figure 13: Reliability diagrams across operational thresholds. Each subfigure contains reliability diagrams under different choices of

\theta_{\mathrm{eval}}

: (a) latent space and (b) pixel space. As the threshold becomes looser, the observed correctness generally moves closer to the predicted confidence, although the latent-space probe remains more overconfident near the main operating point.

Figure 14: Sensitivity to confidence aggregation and probe conditioning. (a–b) Frame- and task-level Spearman correlations under mean, maximum, percentile, and top-

k

aggregation in latent and pixel spaces. (c) High-error detection and spatial localization under different probe conditioning thresholds

\theta_{\mathrm{cond}}

. (d) ECE, Brier score, and mean confidence under the same thresholds. Unlike the operational threshold

\theta_{\mathrm{eval}}

, changing

\theta_{\mathrm{cond}}

regenerates the confidence maps.

Scoring	Weighting	Reconstruction ( $\uparrow$ )	Scene ( $\uparrow$ )	Motion ( $\uparrow$ )	Semantics ( $\uparrow$ )
Base EVAC		0.5655 (-17.8%)	0.8757 (-3.2%)	0.0014 (-99.6%)	0.4873 (-14.3%)
Base EVAC (Warmup v1)		0.6878 (0.0%)	0.9047 (0.0%)	0.3205 (0.0%)	0.5690 (0.0%)
Selection-only retraining: no additional weighting after selection
Random	None	0.6409 (-6.8%)	0.8524 (-5.8%)	0.3310 (+3.3%)	0.4872 (-14.4%)
RoboReward 1	None	0.6712 (-2.4%)	0.8470 (-6.4%)	0.3360 (+4.8%)	0.6022 (+5.8%)
GVL 2	None	0.7088 (+3.1%)	0.9057 (+0.1%)	0.3393 (+5.9%)	0.5913 (+3.9%)

Appendix

Table 4: Aggregated active-learning results. Each entry reports the aggregated mean followed by its relative change from EVAC-v1. Results for learned scoring methods are averaged over three seeds.

Scoring	Weighting	$\Delta$ Reconstruction ( $\uparrow$ )	$\Delta$ Scene ( $\uparrow$ )	$\Delta$ Motion ( $\uparrow$ )	$\Delta$ Semantics ( $\uparrow$ )
Base EVAC		[-0.133, -0.112] (-0.122)	[-0.038, -0.020] (-0.029)	–	[-0.113, -0.049] (-0.081)
Base EVAC (Warmup v1)		–	–	–	–
Selection-only retraining: no additional weighting after selection
Random	None	[-0.055, -0.039] (-0.047)	[-0.060, -0.045] (-0.052)	[-0.035, +0.056] (+0.011)	[-0.112, -0.046] (-0.079)
RoboReward 1	None	[-0.020, -0.013] (-0.017)	[-0.063, -0.052] (-0.058)	[-0.006, +0.038] (+0.015)	[+0.016, +0.053] (+0.035)
GVL 2	None	[+0.018, +0.024] (+0.021)	[-0.001, +0.003] (+0.001)	[-0.008, +0.046] (+0.019)	[+0.003, +0.042] (+0.023)

Appendix

Table 5: Paired-bootstrap improvements over EVAC-v1. For each learned scoring method, episode-level paired differences from seeds 42, 3407, and 123 are pooled before bootstrap resampling. Each entry gives the 95% confidence interval followed by the paired mean in parentheses.

Scoring	Weighting	Reconstruction ( $\uparrow$ )		Scene ( $\uparrow$ )	Semantics ( $\uparrow$ )			Motion ( $\uparrow$ )
Scoring	Weighting	PSNR ( $\uparrow$ )	SSIM ( $\uparrow$ )	Scene Cons. ( $\uparrow$ )	Logics ( $\uparrow$ )	Sem.-CLIP ( $\uparrow$ )	Sem.-BLEU ( $\uparrow$ )	Traj-HSD ( $\uparrow$ )	Traj-Dyn ( $\uparrow$ )	Traj-nDTW ( $\uparrow$ )
Base EVAC		0.5532	0.5778	0.8757	0.4298	0.8523	0.1799	0.0007	0.0001	0.0006
Base EVAC (Warmup v1)		0.6446	0.7309	0.9047	0.5537	0.8824	0.2708	0.1045	0.0721	0.1439
Selection-only retraining: no additional weighting after selection
Random	None	0.5968	0.6849	0.8524	0.3554	0.8689	0.2372	0.1067	0.0725	0.1518
RoboReward 1	None	0.6276	0.7067	0.8712	0.6322	0.8895	0.2832	0.1076	0.0662	0.1501

Appendix

Table 6: Detailed normalized active-learning results for seed 42.

Scoring	Weighting	Reconstruction ( $\uparrow$ )		Scene ( $\uparrow$ )	Semantics ( $\uparrow$ )			Motion ( $\uparrow$ )
Scoring	Weighting	PSNR ( $\uparrow$ )	SSIM ( $\uparrow$ )	Scene Cons. ( $\uparrow$ )	Logics ( $\uparrow$ )	Sem.-CLIP ( $\uparrow$ )	Sem.-BLEU ( $\uparrow$ )	Traj-HSD ( $\uparrow$ )	Traj-Dyn ( $\uparrow$ )	Traj-nDTW ( $\uparrow$ )
Base EVAC		0.5532	0.5778	0.8757	0.4298	0.8523	0.1799	0.0007	0.0001	0.0006
Base EVAC (Warmup v1)		0.6446	0.7309	0.9047	0.5537	0.8824	0.2708	0.1045	0.0721	0.1439
Selection-only retraining: no additional weighting after selection
Random	None	0.5968	0.6849	0.8524	0.3554	0.8689	0.2372	0.1067	0.0725	0.1518
RoboReward 1	None	0.6262	0.7290	0.7985	0.6488	0.8863	0.2775	0.1089	0.0732	0.1554

Appendix

Table 7: Detailed normalized active-learning results for seed 3407.

Scoring	Weighting	Reconstruction ( $\uparrow$ )		Scene ( $\uparrow$ )	Semantics ( $\uparrow$ )			Motion ( $\uparrow$ )
Scoring	Weighting	PSNR ( $\uparrow$ )	SSIM ( $\uparrow$ )	Scene Cons. ( $\uparrow$ )	Logics ( $\uparrow$ )	Sem.-CLIP ( $\uparrow$ )	Sem.-BLEU ( $\uparrow$ )	Traj-HSD ( $\uparrow$ )	Traj-Dyn ( $\uparrow$ )	Traj-nDTW ( $\uparrow$ )
Base EVAC		0.5532	0.5778	0.8757	0.4298	0.8523	0.1799	0.0007	0.0001	0.0006
Base EVAC (Warmup v1)		0.6446	0.7309	0.9047	0.5537	0.8824	0.2708	0.1045	0.0721	0.1439
Selection-only retraining: no additional weighting after selection
Random	None	0.5968	0.6849	0.8524	0.3554	0.8689	0.2372	0.1067	0.0725	0.1518
RoboReward 1	None	0.6260	0.7117	0.8712	0.6364	0.8825	0.2838	0.1174	0.0685	0.1607

Appendix

Table 8: Detailed normalized active-learning results for seed 123.

Feature	Supervision	Brier $\downarrow$	BCE $\downarrow$	ECE $\downarrow$	AUROC $\uparrow$
Bottleneck	Tuned fixed	0.089487	0.288859	0.00567	0.78379
Bottleneck	EMA	0.089551	0.289220	0.00639	0.78487
Decoder	Tuned fixed	0.084181	0.271915	0.00402	0.82237
Decoder	EMA	0.084243	0.272314	0.00643	0.82184

Appendix

Table 9: Controlled feature and supervision comparison on EVAC-v1. All rows use seed 42 and identical probe capacity. Tuned fixed denotes the calibration-selected

[Q_{.05},Q_{.95}]

range. Scores are computed on 250 test episodes with

\tau\in\{150,\ldots,599\}

. Lower Brier, BCE, and ECE and higher AUROC indicate better performance. Boldface marks the best values among the four configurations.

Figure 15: Probe performance at shared thresholds. (a) Brier scores for the four core configurations. (b) EMA-minus-tuned-fixed Brier at each threshold; positive values favor fixed supervision. Fixed and EMA supervision yield closely aligned curves, with a larger separation between decoder and bottleneck features. All configurations use the same binary targets

q_{e,t,(i,j)}(\theta_{k})

, constructed from held-out local latent errors.

Feature	Supervision	Cal. Brier	Test Brier	Test ECE	Test AUROC	Low-noise Brier
Bottleneck	Fixed $[0.20,0.70]$	0.154102	0.153483	0.12854	0.77596	0.090326
Bottleneck	Fixed $[Q_{.10},Q_{.90}]$	0.092173	0.089468	0.00670	0.78338	0.057385
Bottleneck	Fixed $[Q_{.05},Q_{.95}]^{\dagger}$	0.092149	0.089487	0.00567	0.78379	0.057210
Bottleneck	Fixed $[Q_{.20},Q_{.80}]$	0.092776	0.090206	0.01182	0.78185	0.058627
Bottleneck	EMA	0.092239	0.089551	0.00639	0.78487	0.057544
Bottleneck	EMA-Qinit	0.092101	0.089477	0.00694	0.78517	0.057503

Appendix

Table 10: Complete single-seed range ablation. All 12 fits use seed 42. Quantiles

Q_{\rho}

are computed from training errors. The dagger marks the fixed range selected by calibration Brier, both when averaging across feature choices and when selecting separately for each feature. EMA-Qinit starts from

[Q_{.10},Q_{.90}]

. Test columns use

\tau\in\{150,\ldots,599\}

; the final column evaluates the same episodes with

\tau\in\{50,\ldots,199\}

.

	Training-noise range		Low-noise range
Contrast	Difference	95% CI	Difference	95% CI
Decoder $-$ bottleneck (fixed)	$-5.331$	$[-5.657,-4.987]$	$-2.767$	$[-3.156,-2.243]$
Decoder $-$ bottleneck (EMA)	$-5.294$	$[-5.637,-4.934]$	$-3.141$	$[-3.475,-2.762]$
EMA $-$ fixed (decoder)	$+0.072$	$[+0.011,+0.134]$	$-0.016$	$[-0.151,+0.091]$
EMA $-$ fixed (bottleneck)	$+0.035$	$[-0.071,+0.143]$	$+0.358$	$[+0.270,+0.449]$
Feature $\times$ supervision	$+0.037$	$[-0.090,+0.170]$	$-0.374$	$[-0.562,-0.229]$

Appendix

Table 11: Paired task-macro Brier contrasts. All values, including interval endpoints, are multiplied by

10^{3}

. Intervals are percentile 95% task-bootstrap intervals at seed 42. Negative differences favor the first named configuration. Let

B_{a,b}

denote task-macro Brier for feature choice

a

and supervision rule

b

, with

\mathrm{dec}

and

\mathrm{mid}

denoting decoder and bottleneck features. The interaction is

(B_{\mathrm{dec,EMA}}-B_{\mathrm{mid,EMA}})-(B_{\mathrm{dec,fixed}}-B_{\mathrm{mid,fixed}})

.

Figure 16: Feature and supervision effects across tasks. (a) Decoder-minus-bottleneck Brier contrasts under fixed and EMA supervision. (b) EMA-minus-tuned-fixed contrasts for each feature choice. The panels use different horizontal scales to display the respective effect sizes. Error bars indicate percentile 95% task-bootstrap intervals at seed 42.

Figure 17: Noise-range transfer on held-out episodes. Brier (left) and ECE (right) are evaluated on the same episodes with fixed probe parameters and threshold ranges. Decoder features achieve lower Brier in both noise ranges. Low-noise evaluation shows lower Brier but higher ECE due to changes in local-error distributions and target balance.

Figure 18: Alignment of confidence-derived signals with prediction error. (a) Mean within-episode Spearman correlation between normalized patch weights and local latent MAE. (b) Mean within-episode Spearman correlation between spatially averaged frame risk and frame MAE. Risk is averaged over the nine evaluation thresholds before constructing these diagnostics. All variants use the same weighting transformation, and values are computed before EVAC retraining at seed 42.

Figure 19: Qualitative comparison of feature choices and threshold supervision. Two held-out episodes from the hammering task. Each row shows the first ground-truth future frame, pooled latent MAE, and probe responses for decoder features with EMA and tuned fixed supervision, followed by bottleneck features with EMA and tuned fixed supervision. Decoder responses contain finer spatial structure, while EMA and tuned fixed supervision yield similar patterns within each feature choice.

Selection	Reconstruction ( $\uparrow$ )		Scene ( $\uparrow$ )	Semantics ( $\uparrow$ )			Motion ( $\uparrow$ )
Selection	PSNR	SSIM	Scene Cons.	Logics	Sem.-CLIP	Sem.-BLEU	Traj-HSD	Traj-Dyn	Traj-nDTW
Oracle error	0.6458	0.7542	0.8988	0.5826	0.8875	0.2914	0.0985	0.0587	0.1396
Confidence	0.6838	0.7797	0.9322	0.6198	0.8894	0.2754	0.1313	0.0921	0.1780

Appendix

Table 12: Direct-error and confidence-based selection. Both models use optimizer seed 42, 7,298 selected episodes, 4,000 updates, and no loss weighting. Confidence (Selection) uses the seed-42 result in Table 6 , while Table 2 reports the average over three confidence runs. Bold values indicate the better result in each column. All metrics are higher-is-better.

Figure 20: Confidence versus direct-error selection. Bars show Confidence minus Oracle for four aggregate metrics (left) and nine component metrics (right), using the unweighted models in Table 12 . Positive values favor Confidence. Error bars denote paired 95% episode-bootstrap intervals. Most comparisons use 242 common episodes; Sem.-CLIP, Sem.-BLEU, and aggregate Semantics use 190 common episodes.

Budget	Selection	Episodes	Reconstruction $\uparrow$	Scene $\uparrow$	Semantics $\uparrow$	Motion $\uparrow$	Train GPU-h
10%	Random	1,824	0.6341	0.8772	0.5007	0.1701	4.47
10%	Confidence	1,824	0.6995	0.9088	0.5900	0.2502	4.45
20%	Random	3,649	0.6679	0.9003	0.5391	0.2360	4.45
20%	Confidence	3,649	0.6642	0.8812	0.6167	0.1970	4.44
40%	Random	7,298	0.6874	0.8786	0.5576	0.2004	4.44
40%	Confidence	7,298	0.6611	0.8981	0.5987	0.2305	4.47

Appendix

Table 13: Budget sensitivity under 750-update post-training. All models use optimizer seed 42 and no loss weighting. Bold values mark the higher score within each confidence/random pair. Aggregates follow the main-text definitions and use each component’s available mean. Reconstruction, Scene, and Motion cover 62 episodes; CLIP/BLEU cover 56 episodes.

Figure 21: Budget–performance curves under fixed optimization. All metrics are higher-is-better. Confidence and random receive the same 750 updates. In each panel, the gray square marks the 100% full-pool result, and the gray horizontal dashed line extends its mean across budgets for reference. Error bars show 95% episode-bootstrap intervals, using 62 common episodes for most metrics and 56 for CLIP/BLEU. Lines between measured budget points serve as visual guides.

Budget	$\Delta$ Reconstruction $\uparrow$	$\Delta$ Scene $\uparrow$	$\Delta$ Semantics $\uparrow$	$\Delta$ Motion $\uparrow$
10%	$0.0655\ [0.0534,0.0772]$	$0.0316\ [0.0229,0.0396]$	$0.0914\ [0.0247,0.1563]$	$0.0801\ [0.0293,0.1391]$
20%	$-0.0037\ [-0.0186,0.0112]$	$-0.0190\ [-0.0296,-0.0089]$	$0.0768\ [0.0119,0.1425]$	$-0.0389\ [-0.1023,0.0185]$
40%	$-0.0263\ [-0.0411,-0.0120]$	$0.0195\ [0.0031,0.0362]$	$0.0440\ [-0.0164,0.1021]$	$0.0301\ [-0.0570,0.1390]$

Appendix

Table 14: Paired confidence-minus-random differences. Each entry gives the paired mean and its 95% interval from 10,000 episode-bootstrap resamples. Larger differences favor confidence. Reconstruction, Scene, and Motion use 62 common episodes; Semantics uses the 56-episode intersection of its three components. This common-episode calculation can differ slightly from subtracting the component-wise aggregates in Table 13 .

Setting	Value
Task representatives / acquisition score	1 per task / $S_{\mathrm{mean}}$
Rank-based allocation multiplier	$0.25$ – $2.0$
Decoder index $\ell$	5 (zero-based)
Frame-and-patch mixing $\alpha$	0.5
Confidence strength $\lambda_{\mathrm{conf}}$	1.0
Weighting warmup $s_{\mathrm{warm}}$	1,000 updates

Appendix

Table 15: Selection and retraining settings. The weighting parameters apply to the weighted experiments in the main text. The new Oracle and budget-sweep models use unweighted losses.

Episode	Variant	Reconstruction ( $\uparrow$ )	Scene ( $\uparrow$ )	Semantics ( $\uparrow$ )	Motion ( $\uparrow$ )
place burger fries (ep130)	Base EVAC	0.4592 (-27.3%)	0.7890 (-10.0%)	0.3591 (-28.9%)	0.0000 (-100.0%)
	EVAC-v1	0.6318 (0.0%)	0.8770 (0.0%)	0.5050 (0.0%)	0.4570 (0.0%)
	EVAC-v2 Frame	0.6618 (+4.7%)	0.9430 (+7.5%)	0.5221 (+3.4%)	1.0830 (+137.0%)
	EVAC-v2 Fr.+Patch	0.7020 (+11.1%)	0.9370 (+6.8%)	0.5486 (+8.6%)	1.3080 (+186.2%)
place empty cup (ep294)	Base EVAC	0.4605 (-38.6%)	0.7120 (-27.0%)	0.5454 (+2.0%)	0.0000 (-100.0%)
	EVAC-v1	0.7503 (0.0%)	0.9750 (0.0%)	0.5349 (0.0%)	0.5600 (0.0%)

Appendix

Table 16: Aggregated evolution on six representative RoboTwin2.0 episodes. Each entry reports the normalized aggregate followed by its relative change from EVAC-v1. Green and red percentages indicate improvement and degradation, respectively. For Motion, relative changes are omitted when the EVAC-v1 score is zero. Green, yellow, and red cells indicate the best, second-best, and third-best values within each episode.

Figure 22: Training evolution on place_burger_fries (ep130). EVAC-v1 develops a strong blue color shift and the tray contents collapse from the middle of the rollout. EVAC-v2 Frame removes most tray corruption, but the left arm hallucinates an object instead of grasping the burger and the object held by the right arm is visibly deformed. EVAC-v2 Fr.+Patch correctly grasps and places the burger while preserving the fries most faithfully.

Figure 23: Training evolution on place_empty_cup (ep294). In both EVAC-v1 and EVAC-v2 Frame, the arm only partially appears near the image boundary and fails to grasp the cup at the beginning; EVAC-v2 Frame additionally deteriorates toward the end of the rollout. EVAC-v2 Fr.+Patch correctly grasps the cup from the boundary, places it on the plate, and maintains a coherent scene throughout the prediction.

Figure 24: Training evolution on put_object_cabinet (ep282). EVAC-v1 shows moderate color bias and eventually produces a floating arm together with a corrupted cabinet. EVAC-v2 Frame does not resolve these failures: the blue box in the upper-left region disappears and the arm is generated at an incorrect location. EVAC-v2 Fr.+Patch preserves the objects, cabinet structure, and robot geometry most consistently.

Figure 25: Training evolution on stack_blocks_two (ep325). EVAC-v1 exhibits strong color flickering, and the lower blue block disappears when the green block is lifted and stacked. EVAC-v2 Frame largely removes the color shift but fails to keep the green block attached to the gripper and consequently misses the stacking interaction. EVAC-v2 Fr.+Patch resolves both the object-persistence and grasping failures.

Figure 26: Training evolution on stack_bowls_three (ep087). EVAC-v1 suffers from pronounced color drift and repeated red flickering, followed by severe object corruption late in the rollout. EVAC-v2 Frame removes the color shift but still produces substantial bowl deformation, disappearance, and reappearance. EVAC-v2 Fr.+Patch better preserves the bowl geometry and maintains object persistence through the later frames.

Figure 27: Training evolution on turn_switch (ep310). EVAC-v1 produces a reddish robot arm, a bluish background, and implausible floating-arm geometry. EVAC-v2 Frame substantially reduces the color bias, but the arm shape remains distorted. EVAC-v2 Fr.+Patch further improves the local arm geometry and produces an embodiment substantially closer to the ground truth.

Episode	Variant	Reconstruction ( $\uparrow$ )		Scene ( $\uparrow$ )	Semantics ( $\uparrow$ )		Motion ( $\uparrow$ )
Episode	Variant	PSNR ( $\uparrow$ )	SSIM ( $\uparrow$ )	Scene Cons. ( $\uparrow$ )	Sem.-CLIP ( $\uparrow$ )	Sem.-BLEU ( $\uparrow$ )	Traj-HSD ( $\uparrow$ )	Traj-Dyn ( $\uparrow$ )	Traj-nDTW ( $\uparrow$ )
place burger fries (ep130)	Base EVAC	0.4413	0.477	0.789	0.7183	0.000	0.000	0.000	0.000
	EVAC-v1	0.5957	0.668	0.877	0.7879	0.222	0.095	0.001	0.361
	EVAC-v2 Frame	0.6777	0.646	0.943	0.8121	0.232	0.545	0.024	0.514
	EVAC-v2 Fr.+Patch	0.6980	0.706	0.937	0.8182	0.279	0.761	0.002	0.545
place empty cup (ep294)	Base EVAC	0.5100	0.411	0.712	0.8448	0.246	0.000	0.000	0.000

Appendix

Table 17: Detailed normalized EWMBench results for the qualitative evolution study. All metrics are higher-is-better. Green, yellow, and red indicate the best, second-best, and third-best distinct values within each episode, respectively.

Figure 28: More results of qualitative confidence visualization. Best-ranked example (top 1) by patch-level Spearman correlation.

Figure 29: More results of qualitative confidence visualization. Second-highest-ranked example by patch-level Spearman correlation.

Figure 30: More results of qualitative confidence visualization. Lowest-ranked example (top 1) by patch-level Spearman correlation.

Figure 31: More results of qualitative confidence visualization. Second-lowest-ranked example by patch-level Spearman correlation.

Property	Metric	Result
Multi-scale ranking	Patch / Frame / Episode Spearman ( $\uparrow$ )	$0.540/0.590/0.595$
High-error detection	AUROC / AUPRC@top- $5\%$ ( $\uparrow$ )	$0.761/0.146$
Spatial agreement	Top- $5\%$ IoU ( $\uparrow$ )	$0.130$
Temporal stability	Adjacent-frame IoU ( $\uparrow$ ) / Flicker ( $\downarrow$ )	$0.740/0.005$
Temporal alignment	Peak correlation ( $\uparrow$ ) / $\|\mathrm{lag}\|$ ( $\downarrow$ )	$0.602/\approx 0$

ConfAL-WM: Confidence-Guided Active Learning for Action-Conditioned World Models

Abstract

Figures & tables

Appendix

Action-Conditioned World Models

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Cross-Embodiment Robot Foundation World Models with Latent Actions

World-Action Models for Robot Learning and Control: A Survey

Abstract

Action-Conditioned World Models

Explore similar work

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