SimForge: Agentic Generation of Simulation-Ready Worlds and Demonstrations for Robot Learning

Generating a simulation-ready robot-learning world from language requires two distinct execution decisions: which construction stage should handle a failure, and how semantic intent should be expanded into many geometric instances. We present \simforge, an agentic text-to-scene system built on a Graph-Native Harness. Within scene generation, a registered execution graph separates specialist execution from scene-local control: deterministic issue ownership and hard acceptance gates route failures, while bounded language model recommendations can continue or roll back only along registered transitions and retained checkpoints. For dense placement, the model specifies object groups, target quantities or density, support regions, and arrangement intent; geometry programs expand and validate instance poses using measured assets and supports. This arrangement-level interface avoids explicit per-instance pose enumeration in model outputs, addressing the observed context/OOM bottleneck while leaving geometric and simulation costs instance-dependent. Processed assets are separated from scene instances for targeted insertion, material or appearance changes, rigid or articulated substitution, and scene/task variants. An independent PolicyForge pipeline binds accepted worlds to tasks and robots and validates teacher demonstrations. On the common SceneEval scene-quality protocol, with the specified physics settings for COL and STB, the full system attains 89.4% and 89.7% count agreement at room and house level, respectively, with 0% detected collision and 100% stability. Internal controls separate the runtime effects of routing and batch placement. Under matched policy-training and evaluation conditions on the same LIBERO-Plus evaluation set with a common 13K checkpoint, augmented SimForge data reach 87.84% overall and 86.11% on Goal, improving over the Native reference by 11.87 and 31.28 percentage points, respectively. On LIBERO-Pro, the recorded augmented SimForge cohort reaches 48.7% overall at 13K, with effects that vary across generalization dimensions.