Vision-Language-Action (VLA) policies often rely strongly on the camera viewpoints seen during training, causing substantial performance degradation when deployed from unseen perspectives. Collecting demonstrations from sufficiently diverse physical viewpoints is expensive and still provides only sparse coverage of the viewpoint space. We introduce InfiNoVA, a data-augmentation framework that converts synchronized multi-camera demonstrations into a dense distribution of geometrically consistent training views. InfiNoVA reconstructs each manipulation trajectory as a time-varying 3D Gaussian representation and renders novel observations from sampled camera poses while preserving the original state-action correspondence. This explicit scene representation improves frame-level fidelity and temporal consistency while reducing task-critical hallucinations observed in generative novel-view synthesis. Across four real-world manipulation tasks, policies trained with InfiNoVA achieve 5.4x higher average success under unseen randomized viewpoints than both VISTA-based augmentation and the unaugmented policy. InfiNoVA further achieves 1.7x higher success than training directly on all five physical camera views. These results show that dense, geometrically grounded viewpoint augmentation provides a practical route toward camera-robust robot policies without modifying the underlying policy architecture.
Figures & tables
Fig. 2: Multi-view reconstruction and novel-view sampling. Red cameras are used for reconstruction; green cameras denote sampled novel views.
Fig. 3: Qualitative comparison of novel-view synthesis across the four manipulation tasks. Rows correspond to competing synthesis methods. Red ellipses highlight representative task-critical artifacts in VISTA.
Method
PSNR ↑
SSIM ↑
LPIPS ↓
Hunyuan 3D [ 34 ]
7.48
0.693
0.596
DepthSplat [ 35 ]
13.27
0.809
0.517
GLD [ 33 ]
15.97
0.753
0.485
VISTA [ 13 ]
15.13
0.820
0.459
InfiNoVA (Ours)
16.65
0.841
0.388
TABLE I: Quantitative comparison of novel-view rendering quality. Higher PSNR and SSIM indicate better reconstruction fidelity, while lower LPIPS indicates better perceptual similarity.
Task
GLD
VISTA
InfiNoVA
Pick-and-Place
25615
1115
318
Sweep
35119
1529
437
Sort
39026
1693
434
Stack
45056
1961
560
Average/episode
36204
1575
437
TABLE II: Novel-view synthesis time comparison (in seconds).
Method
FeaCD ↓
ReStraV- Δθ ( ∘ ) ↓
Hunyuan 3D [ 34 ]
0.9780
6.09
DepthSplat [ 35 ]
0.969
5.19
VISTA [ 13 ]
0.984
7.16
InfiNoVA (Ours)
0.915
3.49
TABLE III: Temporal consistency averaged across the four manipulation tasks. Lower values indicate better agreement with the ground-truth trajectories.
Fig. 4: Temporal consistency across three timesteps ( t90 , t494 , and t512 ) sampled from the same Pick-and-Place episode. Red ellipses highlight VISTA’s temporal inconsistencies.
Method
Pairwise LPIPS ↓
LPIPS to GT ↓
GLD [ 33 ]
0.5428
0.4810
VISTA [ 13 ]
0.2765
0.5002
InfiNoVA (Ours)
0.1406
0.466
TABLE IV: Values report average pairwise LPIPS and mean LPIPS against ground truth over 5 generations for each of 5 observations.
Fig. 5: Policy success rates across four manipulation tasks.
Vision-language-action (VLA) policies have shown strong potential for general-purpose manipulation, yet they often fail on novel, out-of-distribution objects whose appearance or geometry deviates from the training distribution. The standard remedy is to collect multi-view teleoperation data for every failure case, but this scales poorly in both cost and time. We introduce Pose6DAug, a failure-driven data augmentation framework that turns a policy's own successful episodes into targeted demonstrations for its failure modes, without any new data collection. Our key insight is that each successful episode already encodes a physically valid action trajectory together with calibrated multi-view observations. By swapping only the manipulated object while preserving this trajectory, we obtain new and physically grounded demonstrations. However, naive 2D video editing breaks multi-view consistency and physical plausibility, particularly under heavy occlusion and egocentric viewpoints. Our method instead operates directly in 3D, anchoring the target object with an explicit mesh driven by a temporally coherent 6D pose trajectory, ensuring geometrically consistent renderings across all camera views. Fine-tuning a VLA on data augmented by our method improves success rates by 16.5% relative to the state-of-the-art baseline on novel objects, while preserving in-distribution performance. These results show that multi-view and physically consistent augmentation is a practical path to scalable VLA generalization.
Visuomotor policies for manipulation have demonstrated remarkable potential in modeling complex robotic behaviors, yet minor alterations in the robot's initial configuration and unseen obstacles easily lead to out-of-distribution observations. Without extensive data collection effort, these result in catastrophic execution failures. In this work, we introduce an effective data augmentation framework that generates visually realistic fisheye image sequences and corresponding physically feasible action trajectories from real-world eye-in-hand demonstrations, captured with a portable parallel gripper with a single fisheye camera. We introduce a novel Gaussian Splatting formulation, adapted to wide FoV fisheye cameras, to reconstruct and edit the 3D scene with unseen objects. We utilize trajectory optimization to generate smooth, collision-free, view-rendering-friendly action trajectories and render visual observations from corresponding novel views. Comprehensive experiments in simulation and the real world show that our augmentation framework improves the success rate for various manipulation tasks in both the same scene and the augmented scene with obstacles requiring collision avoidance.
Chuer Pan, Litian Liang, Dominik Bauer +4
Stanford University · Columbia University · Toyota Research Institute
Real-world robot deployment rarely maintains the training-stage camera setup, where cameras often experience repositioning or remounting depending on actual scenarios. Existing view-robust Vision-Language-Action (VLA) policies tolerate such camera variations only when the camera extrinsics are explicitly provided, making them fragile and hard to use especially when view robustness is critical. We argue that the policy should not be told where the camera is, but rather figure it out by itself. To this end, we introduce Camera-Centric VLA (CamVLA), a new VLA model that decouples manipulation controls from camera geometry by predicting (i) a camera-centric end-effector action expressed in the local camera frame, and (ii) a 6-DoF hand-eye matrix relating cameras to the robot base. A deterministic geometric transformation composes the two predictions into a robot base-frame action. This disentangles how I should move in pose-independent camera-centric action generation from where I am looking from in camera-perspective geometric grounding. The resulting policy is calibration-free, depth-free, and single-view, requiring only a single monocular RGB image as the visual observation and task instruction at deployment. Evaluations in both simulation and real-world robot data show that CamVLA consistently improves success rates across diverse unseen viewpoints. Project page: https://alibaba-damo-academy.github.io/CamVLA/.
Wenhao Li, Xueying Jiang, Quanhao Qian +4
1Nanyang Technological University · 2DAMO Academy, Alibaba Group · 3HuPan Lab +1