Urgent Actions Go First: Urgency-Aware Denoising for Real-Time VLA Control

Zibo Wang; Haochen Han; Pengzhen Ren; Mingtong Dai; Fangming Liu

cs.ROSep 29, 2026

Urgent Actions Go First: Urgency-Aware Denoising for Real-Time VLA Control

Authors: Zibo Wang, Haochen Han, Pengzhen Ren, Mingtong Dai, Fangming Liu

Organizations: Pengcheng Laboratory

Abstract

Diffusion and flow-matching Vision-Language-Action (VLA) policies generate action chunks through iterative denoising, incurring substantial inference latency that severely limits real-time robotic control. Existing acceleration methods treat an action chunk as a monolithic computational unit, ignoring a crucial physical reality of receding-horizon control: actions are generated jointly but consumed sequentially, resulting in inherently heterogeneous execution urgencies. We exploit this asymmetry to introduce Urgency-Aware Denoising (UAD), a novel inference-time framework that allocates denoising computation according to when each action is physically needed. UAD releases time-critical urgent actions after fewer denoising steps while overlapping the continued background refinement of tail actions with physical execution. However, heterogeneous denoising introduces two key challenges: early-release errors in urgent actions and trajectory inconsistency in tail actions. UAD elegantly resolves both through two core mechanisms: Trajectory Reconciliation, which reconstructs unified internal state evolution to restore joint denoising coherence without additional model evaluations, and Ghost Action Correction, which leverages non-executed ghost continuations to dynamically compensate for early-release errors across remaining executable actions. Extensive evaluations across multiple VLA architectures, simulation benchmarks, and real-world manipulation tasks demonstrate that UAD achieves up to a 1.89x speedup in average action availability latency while maintaining comparable success rates to vanilla inference with optimal denoising budget, offering a more favorable success-latency trade-off than state-of-the-art VLA acceleration baselines.

Figures & tables

Figure 1: Illustration of Urgency-Aware Denoising (UAD). Insight: Actions are generated jointly but consumed sequentially, creating heterogeneous execution urgencies. Method: UAD releases urgent actions after fewer denoising steps and overlaps the remaining denoising with physical execution. Modules: Urgency-Aware Early Release (UAER) releases urgent actions early to reduce latency, Trajectory Reconciliation (TR) restores consistent joint denoising, and Ghost Action Correction (GAC) compensates for early-release errors using ghost actions. Benefits: UAD reduces action availability latency while maintaining comparable task success rates.

Figure 2: Overview of UAD

Figure 3: Relative

L_{2}

error to vanilla inference (

N^{*}=3

). TR reduces tail action error while ghost states remain close to vanilla.

	SmolVLA + LIBERO-Plus				SmolVLA + Meta-World+				$\bm{\pi_{0.5}}$ + Meta-World+
Method	SR	AAL	$\Delta$ SR	Speedup	SR	AAL	$\Delta$ SR	Speedup	SR	AAL	$\Delta$ SR	Speedup
	(%) $\uparrow$	(ms) $\downarrow$	(pp) $\uparrow$	( $\times$ ) $\uparrow$	(%) $\uparrow$	(ms) $\downarrow$	(pp) $\uparrow$	( $\times$ ) $\uparrow$	(%) $\uparrow$	(ms) $\downarrow$	(pp) $\uparrow$	( $\times$ ) $\uparrow$
Vanilla ( $N^{*}$ )	35.3	21.6	0.0	1.00	70.8	14.4	0.0	1.00	70.5	11.2	0.0	1.00
Vanilla ( $N=1$ )	27.3	11.2	$-8.0$	1.93	60.1	10.8	$-10.7$	1.33	66.6	9.0	$-3.9$	1.24
D3P	36.3	17.6	$+1.0$	1.23	70.3	13.9	$-0.5$	1.04	70.5	11.6	0.0	0.97
RTC	8.8	2.7	$-26.5$	8.00	52.3	1.3	$-18.5$	11.08	59.8	1.5	$-10.7$	7.47

Table 1: Main results across three simulation settings. See Metrics for the definitions of metrics.

Figure 4: Success rate-latency trade-offs across three simulation settings. UAD achieves low AAL while retaining high SR. Broken horizontal axes accommodate RTC’s substantially lower AAL. Error bars indicate Wilson 95% CIs for SR, and episode bootstrap 95% CIs for AAL. Some AAL CIs are too narrow to be visible at the plotted scale.

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

Panel of Figure 8 — Figure 8: Task success rate (red circles, left axis) and action availability latency (blue triangles, right axis) for different numbers of denoising steps $N$ across six model-benchmark settings.

Figure 9: Average tail blocking time for different values of

U

K=1

Figure 10: Real-world evaluation tasks and camera observations. We evaluate five manipulation tasks using front lateral and wrist-mounted camera views. Camera views are cropped.

	SmolVLA		SmolVLA		$\bm{\pi_{0.5}}$		Real-World
	LIBERO-Plus		Meta-World+		Meta-World+		Deployment
Method	AAL	DBL	AAL	DBL	AAL	DBL	AAL	DBL
	(ms) $\downarrow$	(ms) $\downarrow$	(ms) $\downarrow$	(ms) $\downarrow$	(ms) $\downarrow$	(ms) $\downarrow$	(ms) $\downarrow$	(ms) $\downarrow$
Vanilla ( $N^{*}$ )	21.6	120.3	14.4	52.4	11.2	47.1	34.3	216.6
Vanilla ( $N=1$ )	11.2	18.2	10.8	17.9	9.0	24.6	14.9	19.9
D3P	17.6	122.2	13.9	53.7	11.6	36.1	-	-

Appendix

Table 3: Action availability latency (AAL) and DiT blocking latency (DBL) for the evaluated settings and methods.

	SmolVLA					$\bm{\pi_{0.5}}$
Method	Easy	Medium	Hard	Very hard	Overall	Easy	Medium	Hard	Very hard	Overall
Vanilla ( $N^{*}$ )	85.2	56.4	50.0	47.0	70.8	80.7	58.6	68.3	42.0	70.5
Vanilla ( $N=1$ )	76.1	45.5	38.3	29.0	60.1	79.8	54.1	55.0	34.0	66.6
D3P	84.1	57.7	50.8	44.0	70.3	80.2	61.8	68.3	38.0	70.5
RTC	63.2	47.7	30.8	27.0	52.3	69.8	52.3	49.2	33.0	59.8
BAC	80.4	45.9	38.3	31.0	62.8	75.9	39.5	44.2	20.0	58.5

Appendix

Table 4: Success rate (%) on Meta-World+ by task difficulty (easy / medium / hard / very hard: 28 / 11 / 6 / 5 tasks, 20 episodes each).

Method	Button	Grapes	Flower	Tidy	Stack	Overall
Vanilla ( $N=10$ )	100	85	65	95	75	84
Vanilla ( $N=1$ )	100	70	55	75	55	71
UAD (ours)	100	90	65	90	75	84

Appendix

Table 5: Success rate (%) on individual real-world manipulation tasks. Each task is evaluated over 20 episodes. Detailed task descriptions are provided in Fig. 10 .

Figure 11: Success rate-latency trade-offs across three additional simulation settings. Broken horizontal axes accommodate RTC’s substantially lower AAL. Error bars indicate Wilson 95% CIs for SR, and episode bootstrap 95% CIs for AAL. Some AAL CIs are too narrow to be visible at the plotted scale.

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Toward Real-Time VLAs: Stage-Aware Two-Step Flow Denoising and System-Level Evaluation

Di Wu, Rongtian Shen, Ping Liu, Yan Shen+7

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Inference Cost Perception Generation Provenance

Open paper AskPDF arXiv

Vision-language-action (VLA) models face a timing gap between low-rate inference and high-rate robot execution. We characterize this gap through end-to-end latency measurements of model inference and the robot execution chain. Repeated Flow Matching denoising contributes substantially to inference cost, while robot-side delays mainly arise from perception acquisition, communication scheduling, and physical response. Analysis of the velocity field shows relatively stable magnitude and direction in early integration, followed by stronger directional correction near the terminal steps. Based on this stage heterogeneity, we propose two-stage non-uniform denoising, reducing the number of steps from 10 to 2 and model-inference time from 61.557 ms to 21.956 ms. We also develop a distributed real-time VLA framework with independent inference, action-publication, and robot-control rates, modular observation acquisition, and action-provenance logging. Using π0.5 as the baseline, we evaluate six real-time execution methods on a long-horizon physical garment-folding task. Legato performs best overall among training-based methods, while Temporal Smoothing leads among training-free methods; both perform strongly in task success, completion time, action continuity, and acceleration smoothness. Combining two-step denoising with representative execution methods substantially reduces inference cost with a small reduction in task performance. These results motivate joint optimization of model-inference efficiency and robot-system timing.

Abstract

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