Visuomotor policies trained through imitation learning are highly sensitive to changes in camera viewpoint. This is particularly problematic for mobile manipulation (MoMa), where robots rarely execute manipulation from the exact same base pose used during demonstration collection due to navigation uncertainty, localization drift, and environmental constraints. Recent approaches use novel-view synthesis (NVS) to augment demonstrations with unseen viewpoints, but they assume static scenes and therefore fail to re-render the manipulated object during the post-contact interaction phase, leaving the most critical parts of the demonstration unaugmented. We present MOMASPLAT, a novel-view demonstration augmentation framework for mobile manipulation that synthesizes full manipulation trajectories from unseen robot base poses, including dynamic object interactions. Our key insight is that standard MoMa data collection naturally provides the multiview observations needed to reconstruct both the static scene and the manipulated object in 3D. MOMASPLAT combines 3D Gaussian Splatting, feed-forward 3D foundation models, and kinematics-based action retargeting to separately reconstruct the scene and manipulated object, render them from novel viewpoints, and generate geometrically consistent synthetic demonstrations paired with transformed actions. We evaluate MOMASPLAT on five real-world mobile manipulation tasks involving both rigid and articulated objects across three scenes, and validate the fidelity of its synthesized demonstrations against ground truth in simulation. MOMASPLAT substantially improves robustness to unseen base poses, more than doubling the average success rate of the strongest baseline and maintaining non-trivial success at large base displacements where all baselines fail, while reducing the need for largescale real-world data collection.
Figures & tables
Fig. 1 : MoMaSplat . Starting from mobile-manipulation data collected at the original base pose (white and red robot), MoMaSplat synthesizes demonstrations from sampled novel base poses ( yellow -outlined gray robot). The top-right sequence shows the source manipulation demonstration, while the bottom-right shows the corresponding synthesized trajectory, including the dynamic object as it is moved by the robot. Augmenting the source manipulation dataset with these demonstrations enables execution from unseen deployment-time base poses ( blue -outlined gray robot).
Fig. 2 : Overview of MoMaSplat . 3D Reconstruction: We build 3D models of the environment from the navigation and manipulation data. We estimate camera poses via structure-from-motion and fit a 3D Gaussian Splatting model of the static scene, Gscene . For the dynamic objects, we segment each manipulated object and estimate its per-frame pose trajectory with a feed-forward 3D foundation model; since the scene and object poses lie in separate reference frames, we align them with a Sim(3) transformation and fit a separate object model Gobj . Synthesizing Demonstrations from Unseen Base Poses: Given a robot base pose, T~ , sampled around the original demonstration poses (left), we render the scene and object from the new viewpoint, transform the source end-effector trajectory into the new base frame to render the robot from a simulator, and composite the three streams into a photorealistic novel observation. Paired with the transformed actions a~1:Lj , this forms a synthetic demonstration; repeating across sampled poses yields the generated dataset. Imitation Learning: the policy is trained on the union of source and generated demonstrations.
Fig. 3 : Tasks and scenes used in our experiments. We evaluate MoMaSplat on five real-world manipulation tasks across three scenes (top: scenes; bottom: per-task close-ups), covering manipulation of both rigid and articulated objects. The simulated scene and its task, Sim Pick-Place Bottle (right of the dashed lines), are used to measure the fidelity of MoMaSplat ’s generated demonstrations against ground truth.
Fig. 4 : Policy success across base poses. For each task, we evaluate 12 unseen base locations distributed across categorical distance bins that vary per task, with 3 trials per pose, and report success rate by distance to the nearest demonstration pose. The shaded gray region denotes the original base poses; locations 5–50 cm away are unseen deployment poses. MoMaSplat maintains substantially higher success as the base pose shifts away from the source demonstrations (where Source Only is labeled “Original data only”).
Fig. 5 : Spatial distribution of policy evaluation poses. Top-down view of each task showing the table and target-object locations, the original demonstration base poses, and the test base poses used for policy evaluation. Original demonstration poses are marked with circles, while evaluation poses are marked with squares; arrows indicate the robot heading at each pose. Evaluation poses are colored by MoMaSplat policy success rate, providing spatial context for the distance-based results in Fig. 4 .
Region
PSNR (dB) ↑
SSIM ↑
LPIPS ↓
Full image
21.27±1.12
0.902±0.019
0.253±0.030
Scene
26.40±2.18
0.936±0.021
0.248±0.037
Object
18.18±2.15
0.688±0.081
0.341±0.106
Robot
17.43±1.30
0.812±0.057
0.287±0.048
TABLE I : Generated-observation fidelity against simulator ground truth. Mean ± std over 75 generated demonstrations spanning viewpoints with distances of 0.4 – 27.3 cm from the reconstruction views. Full image , object , and robot use every tenth frame ( 2486 total frames), with object and robot restricted to their masks; scene PSNR evaluation uses one frame per demonstration ( 75 in total).
Stage
Parameter
Value
Scene SfM
matcher
sequential (COLMAP)
Scene 3DGS
method / iterations
WildGaussians / 30,000
Scene 3DGS
DSSIM weight
0.2
Metric scale
valid-depth range
(0,9] m, ≥1000 px/frame
Segmentation
detector / tracker
GroundingDINO / SAM2
Frame selection
IoU thresh. / max gap
0.8 / 20 frames
TABLE II : Key hyperparameters across the M o M a S plat pipeline.
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
Suzannah Wistreich, Stephen Tian, Isabella Huang +4
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
Mobile manipulation is a fundamental capability that enables robots to interact in expansive environments such as homes and factories. Most existing approaches follow a two-stage paradigm, where the robot first navigates to a docking point and then performs fixed-base manipulation using powerful visuomotor policies. However, real-world mobile manipulation often suffers from the view generalization problem due to shifts of docking points. To address this issue, we propose a novel low-cost demonstration generation framework named DockAnywhere, which improves viewpoint generalization under docking variability by lifting a single demonstration to diverse feasible docking configurations. Specifically, DockAnywhere lifts a trajectory to any feasible docking points by decoupling docking-dependent base motions from contact-rich manipulation skills that remain invariant across viewpoints. Feasible docking proposals are sampled under feasibility constraints, and corresponding trajectories are generated via structure-preserving augmentation. Visual observations are synthesized in 3D space by representing the robot and objects as point clouds and applying point-level spatial editing to ensure the consistency of observation and action across viewpoints. Extensive experiments on ManiSkill and real-world platforms demonstrate that DockAnywhere substantially improves policy success rates and easily generalizes to novel viewpoints from unseen docking points during training, significantly enhancing the generalization capability of mobile manipulation policy in real-world deployment.
Ziyu Shan, Yuheng Zhou, Gaoyuan Wu +3
Beijing University of Posts and Telecommunications, Beijing, China · Nanyang Technological University, Singapore