Worlds in One Demo: A Synthetic Data Engine for Learning Open-World Mobile Manipulation
WANDA is a synthetic data engine that learns open-world mobile manipulation policies from a single demonstration. It reconstructs background and interaction trajectories, rearranges configurations, uses Corrective State Expansion for robustness, and synthesizes trajectories on diverse 3D worlds, achieving generalization and cross-embodiment support.
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[Submitted on 14 Jul 2026]
Title:Worlds in One Demo: A Synthetic Data Engine for Learning Open-World Mobile Manipulation
View a PDF of the paper titled Worlds in One Demo: A Synthetic Data Engine for Learning Open-World Mobile Manipulation, by Lingxiao Guo and 2 other authors
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Abstract:Learning open-world mobile manipulation policies requires vast data to achieve spatial generalization, long-horizon robustness, and scene generalization. Current prevailing data collection paradigms, teleoperation and UMI, demand prohibitive human effort and cost at scale. To scale beyond the limits of manual data collection, we seek to maximize the value of each human demonstration by scalable data generation. To this end, we introduce WANDA: learning open-World mobile mANipulation from one demonstration via a synthetic DAta engine. WANDA first reconstructs background Gaussian splats and robot-object interaction trajectories from source RGBD observations, as a world substrate for later planning and rendering. It then rearranges contact-rich robot-object interaction segments into extensive spatial configurations, utilizing whole-body motion planning to chain them into new trajectories. To enhance long-horizon robustness, it applies Corrective State Expansion to increase the robot and object state diversity at different stages of mobile manipulation. To unlock cross-environment generalization, trajectories are synthesized on diverse generated 3D worlds from everyday photos. Furthermore, we synthesize photo-realistic observations by compositing rendered robot and object meshes with Gaussian splatting backgrounds. We evaluate our approach on extensive simulation and real-world tasks in various scenes. Experiments show that policies trained with WANDA achieve long-horizon robustness, broad spatial generalization and cross-environment generalization from one real demonstration. Moreover, WANDA naturally supports cross-embodiment data generation, validated by zero-shot deployment on another mobile manipulator with a distinct morphology.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.13154 [cs.RO]
(or arXiv:2607.13154v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.13154
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Lingxiao Guo [view email] [v1] Tue, 14 Jul 2026 18:04:58 UTC (21,318 KB)
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