MobileVISTA: Generative Data Augmentation for Pose Generalization in Mobile Manipulation

Suzannah Wistreich; Stephen Tian; Isabella Huang; Vitor Campagnolo Guizilini; Sergey Zakharov; Katherine Liu; Jiajun Wu

cs.ROOct 5, 2026

MobileVISTA: Generative Data Augmentation for Pose Generalization in Mobile Manipulation

Authors: Suzannah Wistreich, Stephen Tian, Isabella Huang, Vitor Campagnolo Guizilini, Sergey Zakharov, Katherine Liu, Jiajun Wu

Organizations: Stanford University · Toyota Research Institute

Abstract

Mobile manipulators such as humanoid robots are increasingly deployed in dynamic, unstructured environments to perform dexterous manipulation tasks. However, end-to-end manipulation policies trained to imitate demonstration data collected from a single robot pose are brittle: even centimeter-scale deviations in robot pose at deployment can drive ego-centric observations and end-effector trajectories out of the training distribution, leading to sharp drops in performance. We introduce MobileVISTA, a data generation framework that transforms demonstrations captured at canonical poses into diverse, pose-perturbed training data by jointly (1) augmenting egocentric visual observations and (2) retargeting actions to compensate for base pose changes. Unlike prior methods, which assume a camera rigidly mounted off the actuated chain or non-trivial articulated robot geometry largely out of frame, MobileVISTA targets compatibility with egocentric platforms (e.g., humanoids) where the camera is both influenced by and must observe the robot's kinematic chain as it moves. We study MobileVISTA in simulated tasks spanning humanoid and bimanual embodiments, and on a real Galaxea R1 Pro. We find policies trained on MobileVISTA-augmented data demonstrate improved robustness to previously out-of-distribution poses encountered at test time, without additional demonstration collection or a trained generative model. Additionally, we find MobileVISTA's benefit is largest on tested humanoids, where the camera rides the actuated chain and the robot fills much of the frame. Additional videos and appendix can be found on our website: https://mobilevista.github.io

Figures & tables

Fig. 2 : The MobileVISTA framework. Given single-pose demonstrations, MobileVISTA jointly augments observations and actions under a sampled base pose perturbation $\Delta\mathbf{p}\in SE(3)$ . The visual branch removes the robot, reprojects the scene, and re-renders the robot at its retargeted configuration, while the action branch retargets actions in end-effector space via $\Delta\mathbf{p}^{-1}$ to produce $(\tilde{a}_{t})_{t=1}^{T}$ . This augmented dataset is then used to train a pose-robust policy.

Method	Embodiment	Mobile base	Camera on act. chain	Robot vis. past gripper
VISTA [ 5 ]	Single-arm	✗	✗	✓
RoVi-Aug [ 7 ]	Single-arm	✗	✗	✓
ROPA [ 6 ]	Biman.	✗	✗	✓
RoboSplat [ 27 ]	Single-arm	✗	✗	✓
EgoDemoGen [ 8 ]	Biman.	✓	✗	✓
1001 Demos [ 10 ]	Single-arm	✗	✓	✗

TABLE I: Settings targeted by prior work. Embodiment lists every platform class each method is evaluated on, in simulation and on hardware. Only two prior methods consider a mobile base, neither carries the camera on the actuated chain, and none is evaluated on a humanoid. R2RGen synthesizes no images (—).

Fig. 3 : Randomized pose perturbation results. Success rate as a function of evaluation perturbation radius

r_{eval}

(with yaw

\Delta\theta\sim\mathcal{U}(-5^{\circ},5^{\circ})

). MobileVISTA maintains higher success rates than all baselines as perturbation magnitude grows.

Fig. 4 : Ablation results across tasks. Success rate vs. evaluation perturbation radius

r_{eval}

. We compare MobileVISTA against single-modality augmentation— Aug. Images Only, Aug. Actions Only — and an augmentation that omits robot re-rendering at retargeted configurations, Fixed Robot Geom. MobileVISTA outperforms nearly all ablated variants, indicating that both coupled visual–action augmentation and explicit robot re-rendering contribute to pose robustness.

Fig. 5 : Policy performance under MobileVISTA vs. GEN3C-synthesized training data. Success rate vs. evaluation perturbation radius

r_{eval}

, alongside the Simulator (Oracle) upper bound. Policies trained on MobileVISTA -synthesized data outperform those trained on GEN3C-synthesized data across tasks and perturbation magnitudes.

Fig. 6 : Per-trajectory PSNR for synthesized images. Each point shows the PSNR ( $\uparrow$ , dB) of a synthesized trajectory at its sampled base offset from canonical (cm, overhead view). MobileVISTA yields higher PSNR overall, with the largest separation on humanoid tasks. Tasks where the methods are closer in PSNR also show smaller gaps in downstream policy performance.

Panel of Figure 7 — Fig. 7 : Sampled offsets at $\boldsymbol{r_{eval}=15}$ cm in real-world evaluation. Outcomes for each policy across sampled base offsets (cm, overhead view), for book stacking (a) and laundry sort (b). MobileVISTA succeeds across a wide range of offsets, while baselines fail to generalize beyond the canonical pose.

Appendix figures & tables6 assets

Supplementary material from the paper’s appendix.

Appendix

Task	Method	$r{=}0$	$r{=}1$	$r{=}3$	$r{=}5$	$r{=}7$	$r{=}10$	$r{=}15$
Can Sort	Canonical-2D	$0.480_{\pm 0.02}$	$0.500_{\pm 0.09}$	$0.393_{\pm 0.04}$	$0.440_{\pm 0.05}$	$0.327_{\pm 0.03}$	$0.240_{\pm 0.00}$	$0.120_{\pm 0.06}$
	DP3	$0.733_{\pm 0.08}$	$0.713_{\pm 0.06}$	$0.600_{\pm 0.13}$	$0.407_{\pm 0.05}$	$0.327_{\pm 0.06}$	$0.153_{\pm 0.04}$	$0.060_{\pm 0.02}$
	Fine-tuned $\pi_{0.5}$	$0.926_{\pm 0.05}$	$0.906_{\pm 0.01}$	$0.833_{\pm 0.06}$	$0.827_{\pm 0.10}$	$0.780_{\pm 0.09}$	$0.607_{\pm 0.09}$	$0.320_{\pm 0.03}$
	MobileVISTA (Ours)	$\mathbf{0.953_{\pm 0.03}}$	$\mathbf{0.967_{\pm 0.02}}$	$\mathbf{0.933_{\pm 0.02}}$	$\mathbf{0.887_{\pm 0.05}}$	$\mathbf{0.873_{\pm 0.05}}$	$\mathbf{0.793_{\pm 0.06}}$	$\mathbf{0.527_{\pm 0.04}}$
	Simulator (Oracle)	$\textit{0.913}_{\pm 0.02}$	$\textit{0.927}_{\pm 0.05}$	$\textit{0.893}_{\pm 0.01}$	$\textit{0.927}_{\pm 0.03}$	$\textit{0.873}_{\pm 0.05}$	$\textit{0.727}_{\pm 0.01}$	$\textit{0.527}_{\pm 0.08}$
Pouring	Canonical-2D	$0.547_{\pm 0.03}$	$0.413_{\pm 0.01}$	$0.460_{\pm 0.08}$	$0.353_{\pm 0.03}$	$0.200_{\pm 0.09}$	$0.080_{\pm 0.02}$	$0.073_{\pm 0.06}$

Appendix

TABLE III: Detailed per-task success rates under randomized pose perturbation across evaluation radii

r_{eval}\in\{0,1,3,5,7,10,15\}

cm with yaw

\Delta\theta\sim\mathcal{U}(-5^{\circ},5^{\circ})

. Each cell reports the mean

\pm

std over three random seeds (50 evaluation rollouts per seed). Bold indicates the best non-oracle method per row. Simulator (Oracle) is reported as a reference upper bound.

Task	Method	$r{=}0$	$r{=}1$	$r{=}3$	$r{=}5$	$r{=}7$	$r{=}10$	$r{=}15$
Can Sort	Canonical-2D	$0.48\pm 0.02$	$0.50\pm 0.09$	$0.39\pm 0.04$	$0.44\pm 0.05$	$0.33\pm 0.03$	$0.24\pm 0.00$	$0.12\pm 0.06$
	Aug. Images Only	$0.54\pm 0.05$	$0.45\pm 0.09$	$0.36\pm 0.00$	$0.17\pm 0.10$	$0.11\pm 0.04$	$0.03\pm 0.02$	$0.03\pm 0.02$
	Aug. Actions Only	$0.08\pm 0.07$	$0.11\pm 0.08$	$0.09\pm 0.01$	$0.07\pm 0.06$	$0.07\pm 0.05$	$0.07\pm 0.04$	$0.07\pm 0.05$
	Fixed Robot Geom.	$0.80\pm 0.05$	$0.80\pm 0.07$	$0.81\pm 0.05$	$0.64\pm 0.05$	$0.46\pm 0.09$	$0.31\pm 0.10$	$0.17\pm 0.06$
	MobileVISTA (Ours)	$\mathbf{0.95\pm 0.03}$	$\mathbf{0.97\pm 0.02}$	$\mathbf{0.93\pm 0.02}$	$\mathbf{0.89\pm 0.05}$	$\mathbf{0.87\pm 0.05}$	$\mathbf{0.79\pm 0.06}$	$\mathbf{0.53\pm 0.04}$
Pouring	Canonical-2D	$0.55\pm 0.03$	$0.41\pm 0.01$	$0.46\pm 0.08$	$0.35\pm 0.03$	$0.20\pm 0.09$	$0.08\pm 0.02$	$0.07\pm 0.06$

Appendix

TABLE IV : Detailed ablation results across tasks. Success rate (mean

\pm

std over three seeds) vs. evaluation radius

r_{eval}\in\{0,1,3,5,7,10,15\}

cm with yaw

\Delta\theta\sim\mathcal{U}(-5^{\circ},5^{\circ})

. Bold indicates the best method per column within each task.

	Can Sort			Pouring			Box			Drawer
Method	0-5cm	5-10cm	10-15cm	0-5cm	5-10cm	10-15cm	0-5cm	5-10cm	10-15cm	0-5cm	5-10cm	10-15cm
GEN3C	16.29	14.42	13.08	17.74	15.09	13.28	16.37	13.75	11.78	16.32	15.76	14.35
M.Vista	25.02	24.05	23.28	23.71	23.25	22.18	16.47	16.01	14.72	18.67	18.93	18.29

Appendix

TABLE V:

\uparrow

PSNR (dB) between synthesized egocentric views and simulator oracle images across base perturbation magnitudes

r

	GEN3C	M.Vista
Can Sort	14.93	3.81
Pouring	13.65	4.31
Box	14.93	2.39
Drawer	16.85	2.99

Appendix

TABLE VI:

\downarrow

Avg. synthesis time per trajectory (minutes). MobileVISTA is 4-7

\times

faster than GEN3C across tasks.

Fig. 8 : Qualitative comparison of synthesized egocentric observations. For each task, we show the simulator oracle view, the MobileVISTA-synthesized view, and the GEN3C-synthesized view at the same perturbed base pose. MobileVISTA more faithfully preserves scene geometry and object structure.

Hyperparameter	Value
Optimization
Learning rate	1e-4
L2 regularization	0.0
Batch size	16
Steps per epoch	500
Action normalization	min-max

Appendix

TABLE VII : Diffusion Policy hyperparameters used for all MobileVISTA and Canonical-2D policy variants across simulated and real-world tasks.

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