[Submitted on 1 Oct 2026]
Title:Awomo-SimDataEngine: Agentic Simulation-ReadyWorld Generation
View a PDF of the paper titled Awomo-SimDataEngine: Agentic Simulation-ReadyWorld Generation, by Awomo-PhysicalRSI Team and 23 other authors
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Abstract:Generating useful robot-training data requires more than visually plausiblescenes: objects must support interaction, placements must remain physicallyvalid, and tasks must admit repeatable execution. We present\textbf{Awomo-SimDataEngine}, an agentic system that connects asset and scenegeneration to robot demonstration synthesis. Shared asset services providerigid and articulated objects, including structure-grounded part and jointgeneration with ISArt. Scene generation supports two complementary routes:Unravel reconstructs editable scenes from images, while SimForge buildssingle-room and multi-room environments from text. A graph-native harnesscoordinates construction, validation, andbounded repair, routing failures to the responsible module while retainingunaffected scene state. PolicyForge binds validated worlds to tasks and robotembodiments to produce replayable demonstrations. Evaluations cover assetgeometry, scene quality, and downstream policy learning. On MuJoCo-basedLIBERO-Plus, co-training with Isaac Sim demonstrations improves the overallsuccess rate of a World-Action Model (WAM) from $77.17\%$ to $89.43\%$. Goal and spatialsuccess improve by $31.66$ and $6.25$ percentage points, this http URL results support the utility of the generated data for cross-simulatorpolicy training, with more limited gains on long-horizon tasks.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2610.02274 [cs.RO]
(or arXiv:2610.02274v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.02274
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xin Wang [view email] [v1] Thu, 1 Oct 2026 08:38:29 UTC (51,017 KB)
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