Scenario template

simulo create mybot --type scenario

A headless, scripted scene with no learning: no task, no reward, no trainer. Build a world, step it, shut it down. Use this when you want to script a deterministic simulation rather than train a policy — scene construction, a hand-written controller, or a physical plausibility check.

Shape

cartpole = simulo.Asset.from_registry("simulo/robot/cartpole:v1")

class MybotScenario(simulo.Scenario):
    def build(self, scene: simulo.Scene) -> None:
        scene.add(simulo.Terrain.plane(name="ground"), at="/", per_environment=False)
        scene.add(simulo.Light.dome(name="light", ...), at="/", per_environment=False)
        self.robot = simulo.Robot(asset=cartpole, initial_pose=simulo.Pose.identity())
        scene.add(self.robot, at="/World/Robot")

    def on_start(self) -> None: ...    # after build() and runtime init
    def on_step(self) -> None: ...     # every simulation step
    def on_shutdown(self) -> None: ... # cleanup

app = simulo.App("mybot")  # runs on the default Simulo runtime

@app.job(system=simulo.SystemType.TIER_1, timeout=30 * 60)
def simulate(num_steps: int = 1000, num_envs: int = 4) -> dict:
    simulo.run(MybotScenario, device="cuda", headless=True, max_steps=num_steps, num_envs=num_envs)
    return {"steps": num_steps, "num_envs": num_envs}

No @app.entrypoint — simulate is the app’s only job, so simulo run maps --num-steps/--num-envs straight onto its own parameters.

Where to edit

MybotScenario.build — populate the scene: terrain, lights, robots, assets. Robots you add here are automatically replicated across num_envs parallel environments.
on_start / on_step / on_shutdown — your own simulation logic: setup, a per-step control law (a scripted sine sweep, a hand-written controller), and any final logging or saving.
simulate — step count, environment count, resources.

Run it

simulo run app.py --num-steps 1000 --num-envs 4

simulo logs --follow      # watch the deterministic sim step — no reward printed
simulo result             # {"steps": 1000, "num_envs": 4}