Barrier-Constrained Guidance For Real-time Asynchronous VLA Execution

Wenbo Yu; Jifan Li; Wenke Xia; Yuchen Li; Yizhuo Zhang; Pei Ren; Zhengping Che; Di hu

cs.ROOct 10, 2026

Barrier-Constrained Guidance For Real-time Asynchronous VLA Execution

Authors: Wenbo Yu, Jifan Li, Wenke Xia, Yuchen Li, Yizhuo Zhang, Pei Ren, Zhengping Che, Di hu

Organizations: Beijing Forestry University · Beijing Innovation Center of Humanoid Robotics · Gaoling School of Artificial Intelligence, Renmin University of China

Abstract

While asynchronous execution enables VLA models to operate in real-time environments by predicting the next chunk during execution, it inevitably introduces boundary misalignment with the committed action stream. Existing delay-aware solutions address this at the prohibitive cost of extensive retraining, architectural modifications, or fixed-prefix inpainting that inflates computation when low latency is vital. To address these problems, we propose FreeBar, a training-free real-time inference plugin that enables robust asynchronous execution with only marginal additional inference cost by casting chunk alignment in flow-based VLAs as barrier-guided action-space constraint. Specifically, FreeBar introduces a lightweight Control Barrier Function (CBF)-inspired consistency constraint between consecutive action chunks and solves the resulting correction in closed form, avoiding costly backpropagation through the VLA action head. By progressively tightening the consistency bound during generation, FreeBar aligns generated chunks with the committed execution stream while preserving early-stage generative and within-chunk future action flexibility,. Across Kinetix, RoboCasa, and diverse real-world robotic platforms with multiple VLA backbones, FreeBar improves success rates over naive asynchronous execution by 10%--15% on average, with larger gains in highly dynamic scenarios. It also preserves real-time computational efficiency, decreasing inference latency per action chunk over 45% than RTC. Additionally, as a universal plugin, FreeBar remains robust across distinct latency environments, base policies, and hyperparameter settings.

Figures & tables

Figure 1: FreeBar enables smooth, efficient, and robust asynchronous VLA execution. (Top) Swift and fluent robotic responses to dynamic targets (e.g., a moving button). (Bottom) FreeBar ensures smoother kinematics than RTC, efficient inference via lightweight constraint over costly VJP, and robust stability across diverse latencies, frameworks, and hyperparameters. ( ∗ GR00T on RoboCasa, 4 denoising steps.)

Figure 2: Progressive barrier-guided chunk alignment in FreeBar. During asynchronous execution, FreeBar aligns the newly generated action chunk with a time-aligned reference from the previous chunk using closed-form action-space projection. The barrier constraint is progressively tightened across denoising steps and graded along the horizon: proximal actions are strongly aligned, medial actions are softly regularized, and distal actions remain unconstrained.

\lambda\leftarrow\max\left(\frac{b_{k}-g^{\top}v_{k}}{\|g\|_{2}^{2}+\epsilon},0\right)

Algorithm 1 FreeBar Inference

Figure 3: Left : Success rates across varying execution horizons, with the inference delay fixed at 1. FreeBar consistently outperforms RTC and yields substantial improvements over the naive baseline. Right : Success rates across varying inference delays, where the execution horizon is set to

h=\max(d,1)

, with

d

being the delay. FreeBar maintains superior performance over RTC under high-latency conditions. Each data point is averaged over 100 trials.

	Base Policy: GR00T			Base Policy: $\pi_{0}$
Method	$d=1$	$d=2$	$d=3$	$d=1$	$d=2$	$d=3$
Naive	68.0	69.6	68.6	33.8	32.7	31.2
RTC	69.6	70.5	70.0	31.3	32.7	31.7
CBF	68.5	69.8	68.0	31.3	33.3	34.0
FreeBar	72.8	72.3	71.4	36.0	36.5	36.3

Table 1: Left: RoboCasa results (%) with GR00T and

\pi_{0}

as base policies. Right: Hyperparameter robustness across tested configurations, where higher mean performance and lower standard deviation indicate stronger and more stable robustness.

Figure 4: Left : Simulation inference time on RoboCasa with GR00T. FreeBar incurs minimal overhead, comparable to non-horizon-wise CBF, while significantly outperforming RTC. Right : Real-world inference time on the conveyor-belt task with

\pi_{0}

and

\pi_{0.5}

. FreeBar maintains substantially lower latency than RTC.

Benchmark	Method	$\mathbf{d=1}$	$\mathbf{d=2}$	$\mathbf{d=3}$
Kinetix	Temporal Ensembling	41.8	17.2	3.3
Kinetix	FreeBar	87.2	84.5	78.0
RoboCasa	Action-Space Smoothing	70.7	68.9	69.7
	Chunk-Boundary Interpolation	65.5	64.2	69.7
	Uniform Hard Alignment	69.3	68.9	67.9
	FreeBar	72.8	72.3	71.4

Table 2: Comparison with lightweight temporal-consistency baselines. FreeBar consistently outperforms generic smoothing and alignment strategies under varying inference delays

d

Figure 5: Real-world kinematic smoothness comparison between RTC and FreeBar. Top row : Conveyor-belt manipulation (TianGong). Bottom row : Whack-a-mole (Franka Emika robot). Middle & Right : Wrist joint velocity and acceleration. During active manipulation, RTC exhibits remarkable oscillations in both metrics, whereas FreeBar yields substantially smoother profiles.

Method	Conveyor Belt ( $\pi_{0}$ )				Conveyor Belt ( $\pi_{0.5}$ )				Whack-a-Mole ( $\pi_{0}$ )
Method	$h=6$	$h=8$	$h=10$	$h=12$	$h=6$	$h=8$	$h=10$	$h=12$	$h=6$	$h=8$	$h=10$	$h=12$
Naive	56.67	70.00	66.67	63.33	63.33	66.67	76.67	63.33	2.03	5.02	7.05	5.98
RTC	73.33	83.33	80.00	76.67	76.67	80.00	83.33	80.00	10.05	9.98	10.02	8.01
Ours	90.00	86.67	86.67	76.67	93.33	90.00	90.00	83.33	9.61	13.04	12.58	10.62

Table 3: Real-world manipulation results at a fixed inference delay (

d=2

) across varied execution horizons (

h

). We report Conveyor-Belt Manipulation success rates (%) and total Whack-a-Mole hits within 30 s. Our method achieves the highest performance across almost all tested settings.

Appendix figures & tables11 assets

Supplementary material from the paper’s appendix.

Appendix

Suite	# Tasks	Embodiment / Policy	Domain	Main Challenge
Kinetix	12	Flow policy with action chunking	Physics-based simulation	Fast reaction, contact-rich dynamics, delayed execution
RoboCasa	11	GR00T, $\pi_{0}$	Household manipulation simulation	Complex manipulation, visual variation, chunk alignment
Conveyor-Belt Manipulation	1	Dual-arm humanoid, $\pi_{0}$ / $\pi_{0.5}$	Real world	Sorting moving objects under stale observations
Whack-a-Mole	1	Franka Emika, $\pi_{0}$	Real world	Abrupt target changes and reactive striking

Appendix

Table A1: Summary of task suites used in our evaluation. The selected tasks cover diverse embodiments, visual domains, control frequencies, and dynamic interaction patterns.

Panel of Figure 11 — Figure A1: Representative Kinetix tasks from our evaluation suite. The six environments shown cover classic balance control (a, b), reactive manipulation (c, d), and dynamic projectile/locomotion tasks (e, f). In each task the agent must guide a green object to touch a blue target without contacting red obstacles using force-based control. See Table A2 for the full list of all 12 tasks.

ID	Task	Dynamic property evaluated
1	car_launch	Launching a vehicle toward a target; requires precise force magnitude and timing under delayed execution.
2	cartpole_thrust	Balancing an inverted pendulum with active thrust; demands continuous real-time correction to maintain stability.
3	catapult	Catapulting an object to a target; actions have irreversible delayed consequences once the projectile is released.
4	catcher_v3	Catching a moving object mid-flight; requires rapid reaction to a moving target whose position changes during inference.
5	chain_lander	Landing a chain-linked articulated body; coordinated joint control under kinematic constraints and gravity.
6	grasp_easy	Grasping an object in a dynamic scene; requires stable interaction as the object state evolves during inference.

Appendix

Table A2: Description of the 12 Kinetix tasks used in our evaluation.

Panel of Figure 13 — Figure A2: Visualization of the 11 RoboCasa tasks used in our evaluation. The tasks cover visually rich household scenes and robot manipulation, complementing the highly dynamic physics-control tasks in Kinetix.

ID	Task	Manipulation property evaluated
1	CloseFridge	Pushing a fridge door shut; requires precise contact force and smooth deceleration at chunk boundaries to avoid bounce-back.
2	CloseToasterOvenDoor	Closing a small articulated door; constrained joint motion amplifies boundary discontinuity under delayed execution.
3	CoffeeSetupMug	Placing a mug under a coffee machine; multi-step alignment task where early chunk errors propagate to later stages.
4	NavigateKitchen	Mobile-base navigation in a cluttered kitchen; long-horizon trajectory where stale observations cause spatial drift.
5	OpenCabinet	Pulling open a cabinet door; requires stable chunk transitions during the grasp-then-pull sequence.
6	OpenDrawer	Sliding a drawer open; linear constrained motion where jitter at chunk boundaries can jam the drawer track.

Appendix

Table A3: Description of the 11 RoboCasa tasks used in our evaluation.

Figure A3: Top :Conveyor-Belt Manipulation Bottom :Whack-a-Mole Real-world dynamic manipulation tasks. Conveyor-Belt Manipulation evaluates dual-arm sorting of moving objects, while Whack-a-Mole evaluates single-arm reactive control under abrupt target changes. Both tasks expose severe state-action mismatch under asynchronous inference.

Task	Robot platform	Evaluation metric	Main dynamic challenge
Conveyor-Belt Manipulation	Dual-arm humanoid robot station	Sorting success rate over 30 rollouts	Target objects move during inference, so stale observations lead to grasp-location mismatch and sorting failure.
Whack-a-Mole	Franka Emika 7-DOF arm	Number of successful hits within 30 seconds	Active targets change abruptly, requiring rapid reaction while maintaining smooth chunk transitions.

Appendix

Table A4: Detailed description of the real-world tasks. Both tasks require real-time execution under non-stationary scene dynamics.

Method	0.1	0.5	1	5	10	30	50	Mean $\pm$ Std.
RTC	68.0	66.6	–	70.0	69.6	68.6	52.8	$65.93\pm 6.55$
FreeBar	70.6	–	71.4	70.4	70.6	67.6	67.2	$69.63\pm 1.77$

Appendix

Table A5: Complete hyperparameter robustness results on RoboCasa simulation. Each entry reports the average task success rate over the evaluated RoboCasa tasks.

Method	0.1	0.5	1	3	5	8	10	Mean $\pm$ Std.
RTC	53.33	56.67	67.67	53.33	–	46.67	–	$55.53\pm 7.70$
FreeBar	66.70	–	73.30	76.67	83.33	–	73.33	$74.67\pm 6.04$

Appendix

Table A6: Complete hyperparameter robustness results on the real-world Conveyor-Belt Manipulation task with

\pi_{0}

. Each entry reports the sorting success rate (%).

Method	0.1	0.5	1	3	5	8	10	Mean $\pm$ Std.
RTC	53.33	63.33	73.33	56.67	–	50.00	–	$59.33\pm 9.25$
FreeBar	70.00	–	73.30	80.00	80.00	–	76.67	$75.99\pm 4.35$

Appendix

Table A7: Complete hyperparameter robustness results on the real-world Conveyor-Belt Manipulation task with

\pi_{0.5}

. Each entry reports the sorting success rate (%).

Method	0.1	0.5	1	3	5	8	10	Mean $\pm$ Std.
RTC	5.01	6.95	10.08	8.95	–	8.04	–	$7.80\pm 1.92$
FreeBar	10.06	–	12.03	9.95	11.02	–	8.97	$10.40\pm 1.14$

Appendix

Table A8: Complete hyperparameter robustness results on the real-world Whack-a-Mole task with

\pi_{0}

. Each entry reports the number of successful hits within 30 seconds.

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Vision-Language-Action (VLA) models are increasingly promising for robotic manipulation, yet their real-world deployment remains bottlenecked by high inference latency and unstable asynchronous execution. This challenge is particularly pronounced in flow-matching-based VLA models, where action decoding requires multiple iterative steps conditioned on the VLM context. While efficient inference methods improve control frequency and asynchronous methods reduce execution idle time, existing approaches often fail to jointly achieve low-latency inference and accurate, temporally consistent asynchronous execution. We introduce FlashVLA, a streaming action decoding framework that addresses both challenges in a unified formulation. FlashVLA maintains a streaming action buffer with multiple chunks at different noise levels and decodes them using chunk-wise causal attention. This design allows FlashVLA to produce one executable action chunk per inference step. Moreover, its chunk-wise autoregressive formulation implicitly preserves action continuity, enabling smooth asynchronous execution without extra future-state conditioning. Across extensive simulated and real-world experiments, FlashVLA substantially improves inference speed while maintaining strong task performance. It can achieve

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