Training vision-language-action (VLA) policies for humanoid loco-manipulation is constrained by the high cost and complexity of collecting human teleoperation demonstrations. VLA policies fine-tuned in simulators have so far transferred poorly to humanoid loco-manipulation tasks. We present LEGS (Loco-manipulation via Embodied Gaussian Splatting), a hybrid simulator that composites a mesh foreground (robot, objects, props) over a photorealistic 3D Gaussian Splatting (3DGS) background reconstructed from a handheld scene capture. LEGS uses a procedural motion-primitive generator to synthesize labeled demonstrations at scale without human teleoperation, and a two-stage color calibration that aligns renders to the deployment camera. On a Unitree G1 humanoid robot, across three pick-and-place tasks of increasing whole-body difficulty and three VLA backbones ($ψ_0$, $π_{0.5}$, GR00T N1.6), a policy trained purely on LEGS data matches or exceeds one trained on human teleoperation demos on every experiment. It also outperforms a mesh-only baseline trained on the same motions without the calibrated 3DGS frontend. Because motion is recorded independently of appearance, the same demonstrations can be re-rendered for a target scene and objects at over 15x lower cost than teleoperation. When both the objects and the scene change, a policy trained on LEGS-AUG data re-rendered for that target maintains task success, while the teleop baseline fails entirely. Code and videos are available at https://hojjunekim.github.io/legs/.

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