FutureVLA: Joint Visuomotor Prediction for Vision-Language-Action Model

Xiaoxu Xu; Hao Li; Jinhui Ye; Yilun Chen; Jia Zeng; Xinyi Chen; Linning Xu; Dahua Lin

cs.ROMar 11, 2026

FutureVLA: Joint Visuomotor Prediction for Vision-Language-Action Model

Authors: Xiaoxu Xu, Hao Li, Jinhui Ye, Yilun Chen, Jia Zeng, Xinyi Chen, Linning Xu, Dahua Lin, +2 more

Organizations: Beihang University. · Shanghai Artificial Intelligence Laboratory. · University of Science and Technology of China. · The Chinese University of Hong Kong.

Abstract

Predictive foresight is important to intelligent embodied agents. Since the motor execution of a robot is intrinsically constrained by its visual perception of environmental geometry, effectively anticipating the future requires capturing this tightly coupled visuomotor interplay. While recent vision-language-action models attempt to incorporate future guidance, they struggle with this joint modeling. Existing explicit methods divert capacity to task-irrelevant visual details, whereas implicit methods relying on sparse frame pairs disrupt temporal continuity. By heavily relying on visual reconstruction, these methods become visually dominated, entangling static scene context with dynamic action intent. We argue that effective joint visuomotor predictive modeling requires both temporal continuity and visually-conditioned supervision decoupling. To this end, we propose FutureVLA, featuring a novel Joint Visuomotor Predictive Architecture. FutureVLA is designed to extract joint visuomotor embeddings by first decoupling visual and motor information, and then jointly encoding generalized physical priors. Specifically, in the pretraining stage, we leverage heterogeneous manipulation datasets and introduce a Joint Visuomotor Gating mechanism to structurally separate visual state preservation from temporal action modeling. It allows the motor stream to focus on continuous physical dynamics while explicitly querying visual tokens for environmental constraints, yielding highly generalizable joint visuomotor embeddings. Subsequently, in the post-training stage, we employ a latent embeddings alignment strategy, enabling diverse downstream VLA models to internalize these temporal priors without modifying their inference architectures. Extensive experiments demonstrate that FutureVLA consistently improves VLA frameworks.

Figures & tables

Figure 1: Comparison of future guidance paradigms for VLA models. (a) Explicit guidance predicts future video frames. (b) Implicit guidance learns latent vectors to reconstruct changes between sparsely sampled frames. (c) Ours processes continuous multi-frame clips and structurally decouples the latent representation into a visual stream and a motor stream. This visually-conditioned decoupling extracts joint visuomotor embeddings, yielding consistent performance improvements across diverse benchmarks.

Figure 2: Overview of the FutureVLA framework. (a) Joint Visuomotor Pretraining: Continuous video clips are processed by a frozen 3D-VAE into temporal tokens and structurally decoupled into two streams. Visual tokens reconstruct the initial frame $O_{t}$ , while motor tokens, supervised by action chunks, utilize the Joint Visuomotor Gating module (c) based on gated cross-attention, where the motor stream iteratively queries spatial affordances from the visual tokens, yielding physically grounded joint visuomotor embeddings. (b) Joint Visuomotor Embedding Guided VLA Post-training: The frozen model provides joint visuomotor embeddings as future-aware temporal priors. Through latent embedding alignment, the downstream VLA’s intermediate representations are forced to internalize these dynamics.

Settings	Methods	Pick Coke Can	Move Near	Open/Close Drawer	Put in Drawer	Avg.
Visual Matching	OpenVLA-OFT [ 25 ]	72.3	69.6	47.2	0.9	47.5
	$\pi_{0}$ [ 4 ]	87.3	35.0	72.6	16.0	52.7
	GR00T-N1.5 [ 3 ]	51.7	54.0	27.8	7.4	35.2
	Villa-X [ 12 ]	98.7	75.0	59.3	5.6	59.6
	FutureVLA-GT	92.3	74.2	68.5	85.2	80.1
	FutureVLA-OT	97.0	83.8	55.6	74.1	77.6

Table 1: Comparison of different VLA models across four tasks in two SimplerEnv settings on the Google robot. “GT” indicates GR00T-style model, “OT” denotes OFT-style model.

Methods	Put Spoon	Put Carrot	Stack Cube	Put Eggplant	Avg.
OpenVLA-OFT [ 25 ]	12.5	4.2	4.2	100.0	30.2
$\pi_{0}$ [ 4 ]	25.0	16.7	12.5	29.2	20.9
GR00T-N1.5 [ 3 ]	75.3	54.3	57.0	61.3	61.9
UniVLA [ 9 ]	52.8	55.6	2.8	80.6	47.9
Villa-X [ 12 ]	48.3	24.2	19.2	71.7	40.8
FutureVLA-GT	83.3	75.0	29.2	100.0	71.9

Table 2: Comparison of different VLA models across four tasks in the SimplerEnv setting on the WidowX robot.

Methods	Object	Spatial	Goal	Long	Avg.
OpenVLA-OFT [ 25 ]	88.4	84.7	79.2	53.7	76.5
$\pi_{0}$ [ 4 ]	98.8	96.8	95.8	85.2	94.2
GR00T-N1.5 [ 3 ]	97.6	94.4	93.0	90.6	93.9
WorldVLA [ 11 ]	96.2	87.6	83.4	60.0	81.8
DreamVLA [ 49 ]	94.0	97.5	89.5	89.5	92.6
UniVLA [ 9 ]	96.8	96.5	95.6	92.0	95.2

Table 3: Comparison of different VLA models on the LIBERO benchmark.

Figure 3: Visualization in (a) and performance comparison in (b) for four real-robot manipulation tasks. In (a), from top to bottom: (1) Make a burger : The robot first picks up the burger slice on the right and places it on the bread, then picks up the bread slice on the left and places it on top of the burger slice. (2) Insert roses into a pot : The robot picks up the roses from the table and inserts them into the pot. (3) Scoop beans with a spoon : The robot grasps the spoon in the center, scoops beans from the left container, and transfers them into the bowl on the right. (4) Erase handwriting on a whiteboard : The robot erases the black markings on the whiteboard.

Methods	Put Spoon	Put Carrot	Stack Cube	Put Eggplant	Avg.
FutureVLA-GT wo/ JVPM	79.2	58.3	20.8	91.7	62.5
FutureVLA-GT w/ JVPM	83.3	75.0	29.2	100.0	71.9
FutureVLA-OT wo/ JVPM	66.7	58.3	12.5	79.2	54.2
FutureVLA-OT w/ JVPM	75.0	54.2	25.0	100.0	63.6

Table 4: Ablation of Joint Visuomotor Predictive Modeling guidance on the WidowX robot. “w/ JVPM” and “wo/ JVPM” denote whether latent embedding alignment is applied during post-training.

	MT	VT	JVG	Put Spoon	Put Carrot	Stack Cube	Put Eggplant	Avg.
(a)				79.2	58.3	20.8	91.7	62.5
(b)	✓			66.7	54.2	16.7	95.8	58.4
(c)	✓	✓		66.7	62.5	37.5	95.8	65.6
(d)	✓	✓	✓	83.3	75.0	29.2	100.0	71.9

Table 5: Ablation studies of Joint Visuomotor Gate on the WidowX robot. “MT” represents motor tokens, “VT” indicates visual tokens and “JVG” denotes Joint Visuomotor Gating.

Figure 6: Performance comparison of different future guidance styles on the WidowX robot under a unified training framework. Explicit future guidance is evaluated using the WorldVLA-style, while implicit future guidance is evaluated using two representative paradigms: LAPA-style and Villa-X-style.

Figure 7: Visualization of video clips associated with similar latent embeddings. In (a), both clips exhibit a grasping action followed by a front-to-back translation and downward movement. In (b), both clips show a grasping action with a upper-left translation.

Figure 8: Probability density distributions of LAPA-style, Villa-X-style, and our method over the similarity between latent embeddings and executed action chunks. Vertical lines denote the corresponding distribution means.

Appendix figures & tables12 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 9: The composition of datasets used for Joint Visuomotor Pretraining.

Methods	Cam.	Rob.	Lang.	Light	Back.	Noise	Layout	Total
OpenVLA [ 26 ]	0.8	3.5	23.0	8.1	34.8	15.2	28.5	15.6
OpenVLA-OFT [ 25 ]	56.4	31.9	79.5	88.7	93.3	75.8	74.2	69.6
$\pi_{0}$ [ 4 ]	13.8	6.0	58.8	85.0	81.4	79.0	68.9	53.6
WorldVLA [ 11 ]	0.1	27.9	41.6	43.7	17.1	10.9	38.0	25.0
UniVLA [ 9 ]	1.8	46.2	69.6	69.0	81.0	21.2	31.9	43.9
FutureVLA-GT	59.7	66.0	88.2	97.4	97.8	77.3	82.6	79.7

Appendix

Table 6: Comparison of different VLA models on the LIBERO-Plus benchmark. For simplicity, we abbreviate Camera/Robot/Language/Background as Cam./Rob./Lang./Back., respectively. “GT” indicates GR00T-style model, “OT” denotes OFT-style model.

Settings	Methods	Pick Coke Can	Move Near	Open/Close Drawer	Put in Drawer	Avg.
Visual Matching	FutureVLA-GT wo/ JVPM	97.6	69.1	66.2	60.1	73.2
	FutureVLA-GT w/ JVPM	92.3	74.2	68.5	85.2	80.1
	FutureVLA-OT wo/ JVPM	93.0	70.4	54.2	29.6	61.8
	FutureVLA-OT w/ JVPM	97.0	83.8	55.6	74.1	77.6
Variant Aggregation	FutureVLA-GT wo/ JVPM	92.7	68.1	61.9	62.4	71.2
	FutureVLA-GT w/ JVPM	95.7	72.9	52.4	81.2	75.6

Appendix

Table 7: Ablation of JVPM guidance on the Google robot.

Methods	Object	Spatial	Goal	Long	Avg.
FutureVLA-GT wo/JVPM	98.6	98.0	97.0	92.0	96.4
FutureVLA-GT w/JVPM	99.8	98.8	98.6	96.0	98.3
FutureVLA-OT wo/JVPM	98.4	98.0	96.6	93.6	96.6
FutureVLA-OT w/JVPM	99.2	99.6	99.2	94.8	98.2

Appendix

Table 8: Ablation of JVPM guidance on the LIBERO.

Methods	Cam.	Rob.	Lang.	Light	Back.	Noise	Layout	Total
FutureVLA-GT wo/ JVPM	48.8	60.3	87.1	95.5	95.2	75.1	78.3	75.4
FutureVLA-GT w/ JVPM	59.7	66.0	88.2	97.4	97.8	77.3	82.6	79.7
FutureVLA-OT wo/ JVPM	47.1	59.8	87.3	95.9	93.9	73.2	78.9	74.8
FutureVLA-OT w/ JVPM	50.4	68.8	90.0	97.9	95.4	72.6	83.9	78.2

Appendix

Table 9: Ablation of JVPM guidance on the LIBERO-Plus.

Methods	Make Burger	Insert Rose	Scoop Beans	Eraser Handwriting	Avg.
FutureVLA-GT wo/ JVPM	80.0	33.3	46.7	33.3	48.3
FutureVLA-GT w/ JVPM	100.0	46.7	60.0	73.3	70.0
FutureVLA-OT wo/ JVPM	40.0	26.7	40.0	26.7	33.3
FutureVLA-OT w/ JVPM	60.0	40.0	60.0	46.7	51.7

Appendix

Table 10: Ablation of JVPM guidance on real-world robotic tasks.

$\lambda$	Put Spoon	Put Carrot	Stack Cube	Put Eggplant	Avg.
0.5	70.8	75.0	29.2	95.8	67.7
1.0	83.3	75.0	29.2	100.0	71.9
1.5	79.2	70.8	29.2	100.0	69.8

Appendix

Table 11: Impact of the reconstruction loss weight

\lambda

on the WidowX robot.

VQ-GAN	3D-VAE	Cost time (s)	Put Spoon	Put Carrot	Stack Cube	Put Eggplant	Avg.
✓		3.53	79.2	62.5	20.8	95.8	64.6
	✓	3.28	83.3	75.0	29.2	100.0	71.9

Appendix

Table 12: Effect of different video encoding strategies on performance and computational efficiency. “Cost time” denotes the average training time per step.

Reconstruction Target	Put Spoon	Put Carrot	Stack Cube	Put Eggplant	Avg.
Last-frame	75.0	75.0	29.2	95.8	68.8
First-frame	83.3	75.0	29.2	100.0	71.9

Appendix

Table 13: Impact of different reconstruction targets on the WidowX robot.

Figure 10: Visualization of visual perturbations applied to video frames. The left shows the original frame, and the right illustrates different types of data perturbations.

Methods	LAPA-style	Villa-X-style	Ours
Embedding MSE	0.3047	0.0188	0.0054
Origin Action MSE	0.0691	0.0399	0.0371
Noise Action MSE	0.0752	0.0435	0.0398
Action Bias	0.0061	0.0036	0.0027

Appendix

Table 14: Performance comparison of LAPA-style, Villa-X-style, and our method under visual perturbations. “Embedding MSE” measures the discrepancy between latent embeddings before and after noise injection. “Origin Action MSE” and “Noise Action MSE” denote the error between predicted actions and ground-truth actions under clean and perturbed observations, respectively. “Action Bias” quantifies the action deviation induced by visual perturbations.

Frame number	Selected frame indices
2	1, 1, 17, 17, 17
5	1, 6, 10, 14, 17
9	1, 3, 5, 7, 9, 11, 13, 15, 17
17	1–17

Appendix

Table 15: Frame index selection strategy under different frame numbers.

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FoMoVLA: Bridging Visual Foresight and Motion Guidance for Vision-Language-Action Models

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Vision-Language-Action (VLA) models have achieved impressive results in visuomotor policy learning, yet remain fundamentally reactive, mapping current observations and language to actions without explicit forward prediction of world dynamics. Existing visual foresight methods predict future visual states but lack explicit motion guidance: they show where to go but not how to get there. We argue that future feature prediction and sparse point tracking are naturally complementary: the former provides the goal state, while the latter captures the continuous motion path toward it. We propose FoMoVLA, a framework that augments VLA representations with explicit spatio-temporal supervision by jointly learning future feature foresight and sparse 2D point tracking, enhancing the continuous action policy. FoMoVLA introduces compact foresight tokens to decode future feature states, decodes sparse temporal 2D point trajectories to model compact geometric motion, and couples both through a lightweight future-conditioned cross-attention module that enables consistent reasoning between anticipated states and point dynamics. Extensive experiments on LIBERO, RoboCasa GR-1 Tabletop, and LIBERO-Plus demonstrate state-of-the-art performance and strong zero-shot generalization. Project page is available at https://liauto-research.github.io/FoMoVLA.

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