ActionUNet: Improving Robustness of VLA Models with Efficient Multi-scale Fine-tuning

Di Zhu; Ziheng Yan; Fang Wan

cs.CVSep 28, 2026

ActionUNet: Improving Robustness of VLA Models with Efficient Multi-scale Fine-tuning

Authors: Di Zhu, Ziheng Yan, Fang Wan

Organizations: University of Chinese Academy of Sciences

Abstract

Vision-Language-Action (VLA) models have shown great promise for robotic manipulation by mapping multi-modal semantics to physical actions. However, this mapping inherently struggles to align these coarse-grained semantics with fine-grained temporal execution. It leaves VLA models with limited generalization and insufficient robustness in cluttered environments. To overcome this issue, we propose ActionUNet, an efficient multi-scale fine-tuning framework that enhances pre-trained VLA models with minimal computational cost. ActionUNet first constructs a lightweight temporal U-Net within the temporal-aligned action feature space to fuse hierarchical structural priors, effectively bridging the scale gap between semantics and temporal executions. Recognizing that multi-scale modeling can disrupt microscopic temporal continuity and cause mechanical oscillations, ActionUNet then employs a conditional SIREN as a continuous action decoder. Equipped with explicit second-order smoothness constraints, this decoder guarantees temporal continuity and reduces high-frequency motion jitter. By smoothing temporal discontinuities from multi-scale fusion, this continuous formulation reduces mechanical execution failures while preserving the base VLA model's generalization and manipulation robustness. Extensive experiments on RoboTwin 2.0 and LIBERO-Plus benchmarks, together with real-world hard evaluations, demonstrate that ActionUNet significantly improves π0.5 success rates by absolute 9.8%, 6.1%, and 11.4%, respectively, while also generalizing to the regression-based OpenVLA-OFT backbone, highlighting its effectiveness and efficiency as a fine-tuning strategy. Code and implementation details are available at https://github.com/Di-Zhu123/ActionUNet.

Figures & tables

Figure 1 : Illustration of multi-scale temporal dynamics. Per-step end-effector displacements vary significantly across different tasks (left) and over time within a single task “Stack blocks two” (right).

Figure 2 : Overview of ActionUNet. Given input observations and instructions, the VLA backbone first produces temporal-aligned action features. ActionUNet refines these features in the action feature space through multi-scale temporal mapping, and then decodes the fused features with a conditional SIREN into smooth continuous action trajectories.

Figure 3 : Overview of the conditional SIREN decoder. Local SIREN fields conditioned on multi-scale action features are queried over continuous time and fused with Matérn-kernel weights to generate temporally smooth actions.

Task	RDT [ 7 ]		$\pi_{0}$ [ 17 ]		ACT [ 39 ]		DP3 [ 40 ]		$\pi_{0.5}$ [ 4 ]		$\pi_{0.5}$ + ActionUNet
	Easy	Hard	Easy	Hard	Easy	Hard	Easy	Hard	Easy	Hard	Easy	Hard
Single-scale Tasks
Pick Dual Bottles	42	13	57	12	31	0	60	1	65	34	71	39
Handover Mic	90	31	98	13	85	0	100	3	100	55	100	76
Handover Block	45	14	45	8	42	0	70	0	33	13	47	17
Dual-scale Tasks

Table 1 : Task-wise success rates on RoboTwin 2.0 grouped by scale composition.

Method	SPATIAL	OBJECT	GOAL	LONG	Avg.
Diffusion Policy [ 41 ]	78.3	92.5	68.3	50.5	72.4
WorldVLA [ 42 ]	87.6	96.2	83.4	60.0	81.8
SmolVLA [ 43 ]	93.0	94.0	91.0	77.0	88.8
$\pi_{0}$ [ 17 ]	96.8	98.8	95.8	85.2	94.2
$\pi_{0}$ -FAST [ 16 ]	96.4	96.8	88.6	60.2	85.5
UniVLA [ 44 ]	96.5	96.8	95.6	92.0	95.2

Table 2 : LIBERO benchmark results. We report the average success rate (%) across four LIBERO task suites.

Method	Camera	Robot	Lang.	Light	Back.	Noise	Layout	Avg.
OpenVLA [ 2 ]	0.8	3.5	23.0	8.1	34.8	15.2	28.5	15.6
WorldVLA [ 42 ]	0.1	27.9	41.6	43.7	17.1	10.9	38.0	25.0
UniVLA [ 44 ]	1.8	46.2	69.6	69.0	81.0	21.2	31.9	43.9
$\pi_{0}$ [ 17 ]	13.8	6.0	58.8	85.0	81.4	79.0	68.9	53.6
$\pi_{0}$ -FAST [ 16 ]	65.1	21.6	61.0	73.2	73.2	74.4	68.8	61.6
OpenVLA-OFT [ 8 ]	36.0	35.9	67.0	82.1	93.1	47.1	82.5	60.9

Table 3 : Performance under environmental perturbations on LIBERO-Plus. All models are trained on LIBERO and evaluated on LIBERO-Plus. We report success rates (%) under 7 perturbation types.

Task	$\pi_{0.5}$		$\pi_{0.5}$ + ActionUNet
Task	Easy	Hard	Easy	Hard
Place Object Basket	60	42	78	52
Stack Bowls Two	88	62	96	74
Stack Blocks Two	80	68	92	80
Average	76.0	57.3	88.7	68.7

Table 4 : Comparison of our method against the baseline under easy and hard conditions in real-world experiments.

Task	$\pi_{0.5}$		+ U-Net		+ SIREN		+ ActionUNet w/o Matérn		+ ActionUNet
	Easy	Hard	Easy	Hard	Easy	Hard	Easy	Hard	Easy	Hard
Move Can Pot	66	52	64	56	73	53	73	68	77	69
Stack Blocks Two	68	24	75	27	74	24	71	34	74	35
Beat Block Hammer	77	28	65	33	87	30	89	39	89	43
Pick Dual Bottles	65	34	66	33	68	26	67	40	71	39
Average	69.0	34.5	67.5	37.3	75.5	33.3	75.0	45.3	77.8	46.5

Table 5 : Ablation results on four RoboTwin 2.0 tasks. Each task reports success rates under easy and hard settings. All non-baseline variants are built on

\pi_{0.5}

Figure 4 : Scale-conditioned temporal Grad-CAM responses on RoboTwin 2.0. Fine-, mid-, and coarse-resolution temporal features show stronger responses to the corresponding action scales.

Appendix figures & tables22 assets

Supplementary material from the paper’s appendix.

Appendix

Metric	$\pi_{0.5}$	$\pi_{0.5}$ + ActionUNet	Difference
Mean latency / policy call (ms)	$60.947\pm 0.251$	$64.886\pm 0.376$	$+3.939\hskip 8.50012pt(+6.46\%)$

Appendix

Table 6 : Compiled policy-call latency on a single RTX 4090 GPU. We report mean latency with 95% confidence intervals computed over repeated policy-call measurements.

Setting	Average Taken Steps		Latency (s)
Setting	$\pi_{0.5}$	$\pi_{0.5}$ + ActionUNet	$\pi_{0.5}$	$\pi_{0.5}$ + ActionUNet	Difference
Easy	238.80	238.69	$14.554\pm 0.060$	$15.488\pm 0.090$	$+0.934\pm 0.108$
Hard	276.43	264.29	$16.848\pm 0.069$	$17.149\pm 0.099$	$+0.301\pm 0.121$

Appendix

Table 7 : Rollout-level policy computation on the 12 RoboTwin2.0 tasks. Average taken steps are task-level unweighted means over successful rollouts. Latency values are estimated by multiplying the average taken steps by the compiled policy-call latency in Table 6 ; their 95% confidence intervals are propagated from the task-independent policy-call latency measurements. The paired difference is computed as

\pi_{0.5}

+ ActionUNet minus

\pi_{0.5}

latency, and its 95% confidence interval is propagated from the two latency confidence intervals.

Task	Overall	Overall Scale	Stage Sequence	Composition	#Regimes
Adjust Bottle	0.0442	High	H–M	M+H	2
Beat Block Hammer	0.0372	High	H–L	L+H	2
Blocks Ranking RGB	0.0363	Medium	M–L–H–L–H–L–L	L+M+H	3
Blocks Ranking Size	0.0359	Medium	H–L–H–L–H–L–L	L+H	2
Click Alarmclock	0.0412	High	H–M	M+H	2
Click Bell	0.0373	High	H–L	L+H	2

Appendix

Table 8: Task-level and stage-level action scale analysis on 50 tasks. The overall scale is computed from the average end-effector displacement over the entire task. Stage sequence is computed from non-idle stage-level displacement values. All displacement values are reported in m/step.

Figure 7 : Visualization of Place Object Basket task execution with $\pi_{0.5}$ +ActionUNet. The top row shows the keyframes of the task execution in the easy setting, while the bottom row presents the keyframes in the hard setting.

Figure 8 : Visualization of Stack Bowls Two task execution with $\pi_{0.5}$ +ActionUNet. The top row shows the keyframes of the task execution in the easy setting, while the bottom row presents the keyframes in the hard setting.

Figure 9 : Visualization of Stack Blocks Two task execution with $\pi_{0.5}$ +ActionUNet. The top row shows the keyframes of the task execution in the easy setting, while the bottom row presents the keyframes in the hard setting.

Task	$\pi_{0.5}$		+ Layer-wise Pyramid Condition		+ ActionUNet
	Easy	Hard	Easy	Hard	Easy	Hard
Move Can Pot	66	52	71	64	77	69
Stack Blocks Two	68	24	74	25	74	35
Beat Block Hammer	77	28	86	34	89	43
Average	70.3	34.7	77.0	41.0	80.0	49.0
Gain over $\pi_{0.5}$	–	–	+6.7	+6.3	+9.7	+14.3

Appendix

Table 9 : Architecture ablation on where multi-scale features enter the continuous decoder. Layer-wise Pyramid Condition. directly injects U-Net decoder features at different temporal resolutions into different SIREN layers. All non-baseline variants are built on

\pi_{0.5}

Task	$\pi_{0.5}$		+ RTC		+ ActionUNet
	Easy	Hard	Easy	Hard	Easy	Hard
Move Can Pot	66	52	73	64	77	69
Stack Blocks Two	68	24	72	37	74	35
Beat Block Hammer	77	28	71	37	89	43
Pick Dual Bottles	65	34	71	33	71	39
Average	69.0	34.5	71.8	42.8	77.8	46.5

Appendix

Table 10 : Comparison with action smoothing baselines on four RoboTwin2.0 tasks. RTC [ 45 ] performs run-time replanning and soft-mask continuation without changing the learned action representation. All non-baseline variants are built on

\pi_{0.5}

Figure 10 : Ablation study on the Matérn length-scale parameter $\rho$ . Changing $\rho$ within the tested range only introduces minor performance variations, showing that ActionUNet is robust to the choice of the Matérn aggregation bandwidth.

Task	$\pi_{0.5}$		+ $r=1$ Single-scale		+ActionUNet
Task	Easy	Hard	Easy	Hard	Easy	Hard
Move Can Pot	66	52	76	61	77	69
Stack Blocks Two	68	24	73	28	74	35
Beat Block Hammer	77	28	84	33	89	43
Pick Dual Bottles	65	34	65	32	71	39
Average	69.0	34.5	74.5	38.5	77.8	46.5

Appendix

Table 11 : Parameter-matched single-scale control on four RoboTwin 2.0 tasks. The

r=1

variant has the same parameter count as ActionUNet but removes temporal downsampling and cross-scale fusion. All variants are built on

\pi_{0.5}

Task	$\pi_{0.5}$		$\pi_{0.5}$ +ActionUNet
Task	Easy	Hard	Easy	Hard
Single-scale Tasks
Pick Dual Bottles	64.8± 1.0	32.4± 0.9	70.8± 0.6	38.8± 0.4
Handover Mic	99.6± 0.2	56.0± 0.7	99.8± 0.2	78.8± 1.2
Handover Block	34.4± 0.5	13.0± 0.4	48.6± 0.9	16.8± 0.4
Dual-scale Tasks

Appendix

Table 12 : Repeated evaluation results on RoboTwin 2.0. We report mean success rate (%)

\pm

standard error over five repeated evaluations.

Method	Spatial	Object	Goal	Long	Avg.
$\pi_{0.5}$	95.5± 0.45	98.4± 0.28	97.4± 0.85	91.1± 0.95	95.6± 0.35
$\pi_{0.5}$ +ActionUNet	98.2 ± 0.33	99.6 ± 0.33	98.8 ± 0.68	94.0 ± 0.69	97.7 ± 0.27

Appendix

Table 13 : Repeated evaluations on LIBERO. We report mean success rate (%)

\pm

standard error over five repeated evaluations.

Method	Camera	Robot	Lang.	Light	Back.	Noise	Layout	Avg.
$\pi_{0.5}$	48.4± 0.65	48.0± 1.75	67.4± 0.65	93.0± 0.20	87.1± 0.30	51.1± 1.20	81.8± 0.50	66.0± 0.40
$\pi_{0.5}$ +ActionUNet	56.4 ± 0.49	55.2 ± 1.55	74.6 ± 0.51	94.8 ± 0.06	89.7 ± 0.20	60.2 ± 0.97	85.5 ± 0.56	72.0 ± 0.32

Appendix

Table 14 : Repeated evaluations on LIBERO-Plus. We report mean success rate (%)

\pm

standard error over five repeated evaluations.

Task	Easy				Hard
Task	$\hat{p}_{0}$	$\hat{p}_{1}$	STEP	Result	$\hat{p}_{0}$	$\hat{p}_{1}$	STEP	Result
Pick Dual Bottles	.645	.720	153	Sig.	.350	.390	–	FTD
Handover Mic	.995	1.000	–	FTD	.540	.780	46	Sig.
Handover Block	.335	.475	90	Sig.	.120	.185	164	Sig.
Beat Block Hammer	.750	.895	30	Sig.	.280	.435	84	Sig.
Move Can Pot	.660	.770	29	Sig.	.520	.715	20	Sig.

Appendix

Table 15 : Sequential A/B hypothesis testing on RoboTwin 2.0. “Sig.” denotes a statistically significant improvement of

\pi_{0.5}

+ActionUNet over

\pi_{0.5}

, and “FTD” denotes FailToDecide within the maximum budget of 200 trials per policy.

Task	$\pi_{0.5}$		$\pi_{0.5}$ +ActionUNet
Task	Easy	Hard	Easy	Hard
Dual-scale Tasks
Open Laptop	88	59	96	75
Place Dual Shoes	45	12	56	21
Click Alarm	84	17	91	21
Three-scale Tasks

Appendix

Table 16 : Results on 5 additional RoboTwin 2.0 tasks beyond the original 12-task evaluation subset.

Method	Click Alarm	Move Can Pot	Place Can Basket
HiFlow [ 25 ]	69	42	39
$\pi_{0.5}$ +ActionUNet	91	77	57

Appendix

Table 17 : Comparison with HiFlow on three RoboTwin 2.0 tasks under the clean setting. HiFlow results are taken from the corresponding paper.

Method	Spatial	Object	Goal	Long	Avg.
Multi-scale Methods
MINT-30M [ 30 ]	98.6	99.2	97.4	93.2	97.1
FASTer [ 47 ]	98.0	99.4	98.6	95.4	97.9
Hierarchical Methods
Coarse-to-Control [ 48 ]	98.8	100.0	97.8	95.0	97.9
ECHO [ 49 ]	98.3	98.8	98.6	93.5	97.3

Appendix

Table 18 : Comparison with recent multi-scale and hierarchical methods on LIBERO. Results for external methods are taken from the corresponding papers.

Method	Average Success Rate
MINT-30M [ 30 ]	69.5
ECHO [ 49 ]	56.5
OpenVLA-OFT+ActionUNet	64.6
$\pi_{0.5}$ +ActionUNet	71.9

Appendix

Table 19 : Comparison with recent multi-scale and hierarchical methods on LIBERO-Plus. We report average success rate (%).

Task	$\pi_{0.5}$		+ DCT		+ Laplacian		+ ActionUNet
Task	Easy	Hard	Easy	Hard	Easy	Hard	Easy	Hard
Move Can Pot	66	52	70	52	64	56	77	69
Stack Blocks Two	68	24	71	18	66	16	74	35
Beat Block Hammer	77	28	78	29	73	28	89	43
Pick Dual Bottles	65	34	69	30	69	28	71	39
Average	69.0	34.5	72.0	33.0	68.0	32.0	77.8	46.5

Appendix

Table 20 : Comparison with action-space multi-scale decomposition baselines on four RoboTwin 2.0 tasks. DCT decomposes the target action chunk into low-, mid-, and high-frequency components, while the Laplacian variant constructs a multi-resolution residual action pyramid. All variants are built on

\pi_{0.5}

Setting	Method	Success Rate (%) $\uparrow$	Drift (mm) $\downarrow$	Jerk RMS (m/s 3 ) $\downarrow$
Easy	$\pi_{0.5}$	69.0	32.53	32.90
	+ U-Net	67.5	29.07	34.49
	+ SIREN	75.5	28.85	29.43
	+ ActionUNet	77.8	27.60	32.70
Hard	$\pi_{0.5}$	34.5	81.64	48.31
	+ U-Net	37.3	76.05	49.34

Appendix

Table 21 : Quantitative trajectory analysis on four RoboTwin 2.0 tasks. Lower Manipulation Drift and Jerk RMS indicate better spatial accuracy and smoother trajectories, respectively.

Method	Camera	Robot	Lang.	Light	Back.	Noise	Layout	Avg.
$\pi_{0.5}$	48.4± 0.65	48.0± 1.75	67.4± 0.65	93.0± 0.20	87.1± 0.30	51.1± 1.20	81.8± 0.50	66.0± 0.40
+ U-Net	50.8± 0.18	52.4± 0.51	71.3± 0.34	93.8± 0.86	88.7± 0.86	52.9± 0.17	83.1± 0.31	68.4± 0.18
+ SIREN	47.4± 0.23	53.1± 0.30	67.4± 0.25	92.7± 0.55	86.8± 0.42	48.7± 0.63	81.4± 0.53	66.1± 0.17
+ ActionUNet	56.4 ± 0.49	55.2 ± 1.55	74.6 ± 0.51	94.8 ± 0.06	89.7 ± 0.20	60.2 ± 0.97	85.5 ± 0.56	72.0 ± 0.32

Appendix

Table 22 : Module-wise ablation on LIBERO-Plus. All variants are trained on LIBERO and directly evaluated under seven controlled perturbations. We report mean success rate (%)

\pm

standard error over three repeated evaluations.

Task	Method	Success (%) $\uparrow$	Progress $\uparrow$	Jerk RMS $\downarrow$	GT Jerk RMS
Insert Tubes	$\pi_{0.5}$	4.7± 3.1	22.0± 3.2	127.81	50.62
Insert Tubes	+ ActionUNet	14.0 ± 2.0	30.8 ± 0.4	122.99	50.62
Build Tower	$\pi_{0.5}$	26.0± 2.0	37.3± 0.9	119.75	49.54
Build Tower	+ ActionUNet	34.7 ± 2.3	42.5 ± 1.5	109.53	49.54

Appendix

Table 23 : Evaluation on precision-demanding RoboDojo tasks. We report mean success rate and Progress Score

\pm

standard error over three repeated evaluations. Lower Jerk RMS indicates smoother trajectories.

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MaskVLA: Visual Masking Against Trajectory Overfitting of Vision-Language-Action Model

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Diffusion-Based Vision-Language-Actions Overfitting

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Vision-Language-Action (VLA) models integrate vision-language understanding with executable robot actions, enabling end-to-end learning for robot control. However, our empirical analysis reveals that existing models exhibit severe trajectory overfitting when finetuned on limited datasets. To guide the model in effectively utilizing wrist camera information, we propose MaskVLA, a masking-based fine-tuning strategy. By randomly masking a small portion of the main camera's visual information, the model is guided to autonomously learn more fine-grained, task-relevant, and effective visual features. This process leads to the emergence of robust policies, thereby enhancing the model's capability to tackle complex manipulation tasks and improving its generalization performance. Our method has been comprehensively evaluated on RoboTwin 2.0, achieving an average success rate improvement of 23.2% and 16.8% compared to

π_0

and OpenVLA-OFT, respectively. Furthermore, experiments on real-world ALOHA robots also demonstrate the effectiveness of our approach.

Abstract

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