SimVLA: Zero-Shot Sim-to-Real VLA Learning for Mobile Manipulation

Kyoungin Baik; Youngwoon Lee

cs.ROOct 8, 2026

SimVLA: Zero-Shot Sim-to-Real VLA Learning for Mobile Manipulation

Authors: Kyoungin Baik, Youngwoon Lee

Organizations: Yonsei University · Seoul National University

Abstract

Large-scale, diverse datasets have driven the success of LLMs and VLMs. But VLAs for robotics remain limited by the cost and complexity of real-world data collection. While simulation offers a scalable alternative, its potential for sim-to-real VLA learning in mobile manipulation remains largely underexplored. We introduce SimVLA, an end-to-end framework that trains VLAs entirely on synthetic simulation data without teleoperation for mobile manipulation. SimVLA is first pre-trained on two complementary simulation-derived datasets: SimAction, a large-scale robot action dataset spanning 35 diverse mobile manipulation tasks, generated by composing atomic skills, and SimVQA, which leverages privileged simulator state to provide spatial, geometric, and subtask-level visual-language supervision. We further post-train SimVLA on a mixture of SimAction and SimDeploy, a dataset collected from policy rollouts across diverse simulated environments. We evaluate SimVLA on tasks including restocking, pouring, and cleaning, and show zero-shot transfer to real-world mobile manipulation, including real home environments. SimVLA outperforms policies trained on 50 in-domain real-world demonstrations, suggesting that simulation can enable scalable sim-to-real mobile manipulation. We further demonstrate the value of multiple complementary forms of supervision for effectively leveraging simulation in VLA training.

Figures & tables

Dataset	# Task	Mobility	V-L sup.	Data generation	Zero-shot Sim-to-Real
MimicGen ( Mandlekar et al., 2023 )	18	$\bigcirc$	✗	Teleoperation & Augmentation	✗
RoboGen ( Wang et al., 2024 )	106	$\bigcirc$	✗	Motion planning & Optimization & RL	✗
SkillMimicGen ( Garrett et al., 2025 )	18	✗	✗	Teleoperation & Augmentation	✗
DexMimicGen ( Jiang et al., 2025 )	9	$\bigcirc$	✗	Teleoperation & Augmentation	$\bigcirc$
GraspVLA ( Deng et al., 2025 )	1	✗	$\bigcirc$	Motion planning	$\bigcirc$
RoboTwin 2.0 ( Chen et al., 2026 )	50	✗	✗	Motion planning	$\bigcirc$

Table 1: Comparison of simulation data generation methods. “V-L sup.” indicates visual-language supervision beyond action trajectories, such as VQA-style annotations. “Policy rollouts” denotes data collected by rolling out a learned policy in simulation.

Figure 1: Overview of SimVLA Training. During pre-training, SimVLA jointly optimizes continuous action prediction, tokenized action prediction, and SimVQA supervision. During post-training, SimVLA is trained on a mixture of SimAction and deployment trajectories from SimDeploy.

Panel of Figure 2 — Figure 2: Five long-horizon mobile manipulation tasks used for real-world evaluation.

Panel of Figure 3 — Figure 3: Real-world evaluation setup. Top: three evaluation environments: ( 3(a) ) mock kitchen, ( 3(b) ) pantry, and ( 3(c) ) real home. Bottom: mock kitchen evaluation conditions: ( 3(d) ) In-Distribution, ( 3(e) ) OOD-pos, and ( 3(f) ) OOD-vis.

Figure 4: Averaged task progress in real-world environments. Mock kitchen includes all five tasks; pantry includes put mug / bowl / bottle in sink ; real home includes put mug in sink / pour bottle to mug . SimVLA maintains robust performance in OOD environments, while the fine-tuned policy degrades substantially. Removing pre-training leads to poor performance, demonstrating its importance for robust generalization.

Figure 5: Averaged task progress across tasks in mock kitchen-ID. Removing either SimVQA or Tokenized SimAction degrades performance across all tasks. SimDeploy yields task-dependent gains in zero-shot deployment performance.

Panel of Figure 6 — Figure 6: Sampled drawer-grasping frames from real-world teleoperation and simulation. Teleoperation trajectories are visually homogeneous, while simulation exhibits greater diversity.

Pick
	MolmoB0T	SimVLA
MolmoSpaces	36.3 $\pm$ 2.8	71.0 $\pm$ 2.9
SimVLA kitchens	9.7 $\pm$ 1.7	91.2 $\pm$ 2.8
PnP
MolmoSpaces	1.0 $\pm$ 0.7	49.0 $\pm$ 3.4
SimVLA kitchens	0.5 $\pm$ 0.5	79.5 $\pm$ 3.1

Table 2: Zero-shot mobile manipulation. Success rate (%); Pick { mug, bottle, apple }, PnP { apple in bowl, mug next to bowl }. SimVLA is post-trained without MolmoSpaces SimDeploy. SimVLA remains robust under partial observability. None of the environments are included in the training dataset.

Appendix figures & tables21 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 7: The five kitchen layout types used in SimVLA: peninsula, L-shaped, U-shaped, island, and single-wall. Each layout is procedurally generated with randomized furniture configurations, object placements, and surface materials.

Figure 8: 40 bowl variants used in SimVLA, spanning diverse shapes, sizes, depths, and surface materials for robust sim-to-real generalization.

Figure 10: Real-world search behavior. Given only a language prompt and RGB observations, the policy executes searching motions until the sink is visible, then proceeds to complete the task.

Figure 11: BODex grasp pose generation and selection GUI.

Figure 12: Massive parallel environments (4096 instances) running simultaneously in Isaac Lab for hybrid system identification.

Objective	L2 mean (mm) $\downarrow$	L2 max (mm) $\downarrow$
None (URDF defaults)	$83.4\pm 50.2$	$120.7\pm 96.0$
Joint-space	$14.7\pm 2.3$	$46.2\pm 9.6$
EEF-space	$13.4\pm 1.7$	$40.8\pm 8.4$
Hybrid (joint + EEF)	$\mathbf{13.3\pm 1.6}$	$\mathbf{40.6\pm 8.1}$

Appendix

Table 3: System-identification ablation. EEF position discrepancy over ten held-out trajectories on both arms;

\pm

denotes standard deviation across trajectories.

ID	Task	Description
1	Move to mug	The robot navigates and positions itself near the mug.
2	Move to bowl	The robot navigates and positions itself near the bowl.
3	Move to bottle	The robot navigates and positions itself near the bottle.
4	Move to drawer	The robot navigates and positions itself in front of the drawer.
5	Put mug in sink	The robot navigates to the mug, grasps it, moves to the sink, and places it into the sink.
6	Put bowl in sink	The robot navigates to the bowl, grasps it, moves to the sink, and places it into the sink.

Appendix

Table 4: Task definitions.

Domain	Randomness
Camera: Position Noise (m)	$\sim\mathcal{N}(0,0.03^{2})$
Camera: Rotation Noise (deg)	$\sim\mathcal{U}(-5,5)$
Kitchen: Types	5
Kitchen: Tables	50
Kitchen: Chairs	31
Kitchen: Cabinet Materials	21

Appendix

Table 5: Domain Randomization Setup.

VQA Function	Question Prompt Prototype	Answer Prototype
high_level_subtask	To complete the task “{task}”, what should the robot do now?	{subtask}
next_subtask	After current subtask {subtask}, what is the next subtask?	{subtask}
object_detection	Is the {object} visible in the front camera?	yes. bbox: {y1,x1,y2,x2} / no
object_3d_information	How far is the {object} from the robot base?	dx {x} dy {y} dz {z}
view_correspondence	Given front bbox {bbox}, what are the wrist camera bboxes?	left {bbox} right {bbox}
object_reachable	Is the robot close enough to manipulate the {object}?	yes / no. move base dx {x} dy {y} dyaw {yaw}

Appendix

Table 6: SimVQA Templates

Hyperparameter	Pretrain	Post-train
Global batch size	256	256
Precision	BF16	BF16
Gradient clipping (L2)	1.0	1.0
Peak learning rate	$5\times 10^{-5}$	$5\times 10^{-5}$
End learning rate	$5\times 10^{-5}$	$5\times 10^{-5}$
LR scheduler	cosine	cosine

Appendix

Table 7: Key hyperparameters for SimVLA training.

Hyperparameter	Value
FAST loss weight	0.1
VQA loss weight	0.1
FAST sampling ratio	0.50
Co-train sampling ratio	0.01
Discrete state input	false
Delta joint actions	true

Appendix

Table 8: Co-training and FAST auxiliary-loss hyperparameters used during SimVLA pretraining.

Stage	Hardware	CPU	System RAM	Storage	Per-run compute	Total compute
Pre-training	4 $\times$ NVIDIA B200 (183GB)	128 CPU cores	1.5 TB	NVMe SSD	$\sim$ 12 days	$\sim$ 1152 GPU-hours
Post-training	4 $\times$ NVIDIA B200 (183GB)	128 CPU cores	1.5 TB	NVMe SSD	$\sim$ 30 hours	$\sim$ 120 GPU-hours

Appendix

Table 9: Compute resources used for SimVLA experiments. All experiments were conducted on an internal GPU cluster. Reported times are approximate wall-clock durations and may vary depending on storage and data-loading throughput.

Model	Gripper $\uparrow$	Subtask $\uparrow$	3D Pos. $\downarrow$	Grasp $\downarrow$	Cross-view IoU $\uparrow$
	L / R	Curr. / Next	(m)	(m)
SimVLA w/o SimVQA	– ∗	– ∗	– ∗	– ∗	– ∗
SimVLA w/o TokSimAction	0.86 / 0.69	0.18 / 0.39	0.50	0.16	0.04
SimVLA w/o both	0.85 / 0.68	– †	– †	– †	– †
SimVLA	1.00 / 0.89	0.69 / 0.77	0.06	0.05	0.61

Appendix

Table 11: Held-out 1,369 SimVQA evaluation. Ablation study on visual reasoning capabilities.

\uparrow

indicates higher is better and

\downarrow

indicates lower is better.

Task	Success Rate
Put mug to sink	76%
Put bowl in drawer	70%
Pour from bottle to mug	76%
Put mug and bowl to sink	38%
Put bowl to sink	62%
Put bottle to sink	64%

Appendix

Table 12: SimVLA success rates on eight long-horizon simulation tasks using Anubis robot. Each task is evaluated over 50 rollouts across five unseen kitchen setups.

Model	Task 1	Task 2	Task 3	Task 4	Task 5
$\pi_{0.5}$ fine-tuned w/ 50 demos	0.55 $\pm$ 0.43 [1pt] (8/20)	0.36 $\pm$ 0.39 [1pt] (5/20)	0.48 $\pm$ 0.38 [1pt] (6/20)	0.45 $\pm$ 0.46 [1pt] (7/20)	0.43 $\pm$ 0.47 [1pt] (7/20)
$\pi_{0.5}$ fine-tuned w/ 200 demos	0.75 $\pm$ 0.44 [1pt] (15/20)	0.66 $\pm$ 0.42 [1pt] (11/20)	0.70 $\pm$ 0.36 [1pt] (10/20)	0.65 $\pm$ 0.43 [1pt] (11/20)	0.63 $\pm$ 0.46 [1pt] (11/20)
SimVLA (w/o pre-train)	0.28 $\pm$ 0.38 [1pt] (3/20)	0.15 $\pm$ 0.16 [1pt] (0/20)	0.15 $\pm$ 0.25 [1pt] (1/20)	0.18 $\pm$ 0.34 [1pt] (2/20)	0.18 $\pm$ 0.29 [1pt] (1/20)
SimVLA (w/o SimDeploy)	0.45 $\pm$ 0.32 [1pt] (3/20)	0.38 $\pm$ 0.31 [1pt] (3/20)	0.45 $\pm$ 0.33 [1pt] (4/20)	0.33 $\pm$ 0.34 [1pt] (2/20)	0.33 $\pm$ 0.34 [1pt] (2/20)
SimVLA	0.60 $\pm$ 0.42 [1pt] (9/20)	0.50 $\pm$ 0.36 [1pt] (6/20)	0.52 $\pm$ 0.38 [1pt] (6/20)	0.55 $\pm$ 0.46 [1pt] (9/20)	0.55 $\pm$ 0.43 [1pt] (8/20)

Appendix

Table 13: Mock Kitchen — ID. The

\pi_{0.5}

model fine-tuned with 200 demonstrations achieves the best performance on all tasks. SimVLA outperforms

\pi_{0.5}

fine-tuned on 50 real-world demonstrations in task progress across all tasks, with comparable or higher binary success. Task progress

\pm

s.d. over 20 trials; binary successes in parentheses.

Model	Task 1	Task 2	Task 3	Task 4	Task 5
$\pi_{0.5}$ fine-tuned w/ 50 demos	0.40 $\pm$ 0.38 [1pt] (4/20)	0.33 $\pm$ 0.37 [1pt] (4/20)	0.40 $\pm$ 0.37 [1pt] (4/20)	0.30 $\pm$ 0.44 [1pt] (5/20)	0.25 $\pm$ 0.41 [1pt] (4/20)
$\pi_{0.5}$ fine-tuned w/ 200 demos	0.45 $\pm$ 0.39 [1pt] (5/20)	0.43 $\pm$ 0.40 [1pt] (6/20)	0.48 $\pm$ 0.35 [1pt] (4/20)	0.35 $\pm$ 0.46 [1pt] (6/20)	0.28 $\pm$ 0.44 [1pt] (5/20)
SimVLA (w/o pre-train)	0.23 $\pm$ 0.34 [1pt] (2/20)	0.17 $\pm$ 0.18 [1pt] (0/20)	0.13 $\pm$ 0.17 [1pt] (0/20)	0.10 $\pm$ 0.26 [1pt] (1/20)	0.25 $\pm$ 0.38 [1pt] (3/20)
SimVLA (w/o SimDeploy)	0.45 $\pm$ 0.36 [1pt] (4/20)	0.36 $\pm$ 0.31 [1pt] (3/20)	0.43 $\pm$ 0.38 [1pt] (5/20)	0.38 $\pm$ 0.32 [1pt] (2/20)	0.50 $\pm$ 0.32 [1pt] (4/20)
SimVLA	0.58 $\pm$ 0.41 [1pt] (8/20)	0.50 $\pm$ 0.36 [1pt] (6/20)	0.52 $\pm$ 0.37 [1pt] (6/20)	0.60 $\pm$ 0.45 [1pt] (10/20)	0.68 $\pm$ 0.41 [1pt] (11/20)

Appendix

Table 14: Mock Kitchen — OOD-Pos. SimVLA is best on task progress across all tasks, and best-or-comparable on binary success. Task progress

\pm

s.d. over 20 trials; binary successes in parentheses.

Model	Task 1	Task 2	Task 3	Task 4	Task 5
$\pi_{0.5}$ fine-tuned w/ 50 demos	0.35 $\pm$ 0.46 [1pt] (6/20)	0.37 $\pm$ 0.39 [1pt] (5/20)	0.40 $\pm$ 0.37 [1pt] (4/20)	0.50 $\pm$ 0.49 [1pt] (9/20)	0.30 $\pm$ 0.41 [1pt] (4/20)
$\pi_{0.5}$ fine-tuned w/ 200 demos	0.45 $\pm$ 0.48 [1pt] (8/20)	0.45 $\pm$ 0.42 [1pt] (7/20)	0.45 $\pm$ 0.39 [1pt] (5/20)	0.53 $\pm$ 0.47 [1pt] (9/20)	0.48 $\pm$ 0.47 [1pt] (8/20)
SimVLA (w/o pre-train)	0.20 $\pm$ 0.34 [1pt] (2/20)	0.13 $\pm$ 0.15 [1pt] (0/20)	0.13 $\pm$ 0.17 [1pt] (0/20)	0.18 $\pm$ 0.29 [1pt] (1/20)	0.08 $\pm$ 0.18 [1pt] (0/20)
SimVLA (w/o SimDeploy)	0.38 $\pm$ 0.36 [1pt] (3/20)	0.29 $\pm$ 0.28 [1pt] (2/20)	0.37 $\pm$ 0.36 [1pt] (4/20)	0.43 $\pm$ 0.34 [1pt] (3/20)	0.43 $\pm$ 0.29 [1pt] (2/20)
SimVLA	0.63 $\pm$ 0.39 [1pt] (9/20)	0.40 $\pm$ 0.34 [1pt] (4/20)	0.47 $\pm$ 0.38 [1pt] (6/20)	0.58 $\pm$ 0.41 [1pt] (8/20)	0.60 $\pm$ 0.38 [1pt] (8/20)

Appendix

Table 15: Mock Kitchen — OOD-Vis. SimVLA achieves the best performance on Tasks 1, 3, 4 and 5, whereas the

\pi_{0.5}

model fine-tuned with 200 demonstrations performs best on Task 2. Relative to OOD-Pos,

\pi_{0.5}

holds up better under OOD-Vis shifts, likely due to image augmentation during its fine-tuning. Task progress

\pm

s.d. over 20 trials; binary successes in parentheses.

Model	Task 1	Task 2	Task 3	Task 4	Task 5
SimVLA w/o SimVQA	0.43 $\pm$ 0.47 [1pt] (7/20)	0.36 $\pm$ 0.41 [1pt] (5/20)	0.37 $\pm$ 0.40 [1pt] (5/20)	0.50 $\pm$ 0.43 [1pt] (7/20)	0.45 $\pm$ 0.43 [1pt] (6/20)
SimVLA w/o TokSimAction	0.40 $\pm$ 0.38 [1pt] (4/20)	0.29 $\pm$ 0.35 [1pt] (3/20)	0.42 $\pm$ 0.37 [1pt] (5/20)	0.43 $\pm$ 0.41 [1pt] (5/20)	0.50 $\pm$ 0.43 [1pt] (7/20)
SimVLA w/o both	0.38 $\pm$ 0.36 [1pt] (3/20)	0.26 $\pm$ 0.35 [1pt] (3/20)	0.37 $\pm$ 0.36 [1pt] (4/20)	0.48 $\pm$ 0.44 [1pt] (7/20)	0.45 $\pm$ 0.43 [1pt] (6/20)
SimVLA	0.60 $\pm$ 0.42 [1pt] (9/20)	0.50 $\pm$ 0.36 [1pt] (6/20)	0.52 $\pm$ 0.38 [1pt] (6/20)	0.55 $\pm$ 0.46 [1pt] (9/20)	0.55 $\pm$ 0.43 [1pt] (8/20)

Appendix

Table 16: Pretraining Ablation , same tasks as submitted, 20 rollouts for each evaluation in mock kitchen-ID. Removing either SimVQA or TokSimAction lowers task progress on every task relative to full SimVLA; removing SimVQA degrades control mainly through less precise grasping. Task progress

\pm

s.d.; binary successes in parentheses.

	Pantry (20 trials)			Real Home (10 trials)
Model	Task 1	Task 4	Task 5	Task 1	Task 3
$\pi_{0.5}$ fine-tuned w/ 50 Mock Kitchen demos	0.10 $\pm$ 0.21 [1pt] (0/20)	0.08 $\pm$ 0.18 [1pt] (0/20)	0.18 $\pm$ 0.24 [1pt] (0/20)	0.20 $\pm$ 0.26 [1pt] (0/10)	0.17 $\pm$ 0.18 [1pt] (0/10)
$\pi_{0.5}$ fine-tuned w/ 200 Mock Kitchen demos	0.23 $\pm$ 0.30 [1pt] (1/20)	0.23 $\pm$ 0.30 [1pt] (1/20)	0.18 $\pm$ 0.29 [1pt] (1/20)	0.35 $\pm$ 0.24 [1pt] (0/10)	0.23 $\pm$ 0.16 [1pt] (0/10)
SimVLA w/o pre-train	0.10 $\pm$ 0.21 [1pt] (0/20)	0.10 $\pm$ 0.26 [1pt] (1/20)	0.08 $\pm$ 0.18 [1pt] (0/20)	0.10 $\pm$ 0.21 [1pt] (0/10)	0.10 $\pm$ 0.16 [1pt] (0/10)
SimVLA	0.60 $\pm$ 0.38 [1pt] (8/20)	0.53 $\pm$ 0.47 [1pt] (9/20)	0.58 $\pm$ 0.44 [1pt] (9/20)	0.70 $\pm$ 0.42 [1pt] (6/10)	0.47 $\pm$ 0.42 [1pt] (3/10)

Appendix

Table 17: Pantry and Real Home. Task progress

\pm

s.d.; binary successes in parentheses. Pantry is evaluated on Tasks 1, 4 and 5 with 20 trials each; Real Home on Tasks 1 and 3 with 10 trials each.

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