Adding a wind field to quadcopter RL training python code

Omniverse Isaac Sim
1

Hello, I am now looking to add a wind field to the quadcopter reinforcement learning training process and implement it in python code. Here is the python code I see.

                # Create the force field prim
                xformPrim = UsdGeom.Xform.Define(stage, primPathName)
                xformPrim.AddTranslateOp().Set(Gf.Vec3f(0.0, 500.0, 0.0))
                prim = xformPrim.GetPrim()

                # Add the wind force fields to the prim
                windApi = ForceFieldSchema.PhysxForceFieldWindAPI.Apply(prim, "wind")
                windApi.CreateDragAttr(1000.0)
                windApi.CreateAverageSpeedAttr(500.0)
                windApi.CreateSpeedVariationAttr(2.0)
                windApi.CreateSpeedVariationFrequencyAttr(3.0)
                windApi.CreateAverageDirectionAttr(Gf.Vec3f(0.0, 0.0, 1.0))
                windApi.CreateDirectionVariationAttr(Gf.Vec3f(0.707, 0.707, 0.0))
                windApi.CreateDirectionVariationFrequencyAttr(Gf.Vec3f(2.0, 2.0, 2.0))
                forceFieldApi = ForceFieldSchema.PhysxForceFieldAPI(windApi, "wind")
                forceFieldApi.CreateEnabledAttr(True)
                forceFieldApi.CreatePositionAttr(Gf.Vec3f(0.0, 0.0, 0.0))
                forceFieldApi.CreateRangeAttr(Gf.Vec2f(-1.0, -1.0))

                forceFieldApi.CreateSurfaceSampleDensityAttr(surfaceSampleDensity)

But I don’t know where I should put them in quadcopter_env.py.

class QuadcopterEnvCfg(DirectRLEnvCfg):
    # env
    episode_length_s = 10.0
    decimation = 2
    action_space = 4
    observation_space = 12
    state_space = 0
    debug_vis = True

    ui_window_class_type = QuadcopterEnvWindow

    # simulation
    sim: SimulationCfg = SimulationCfg(
        dt=1 / 100,
        render_interval=decimation,
        disable_contact_processing=True,
        physics_material=sim_utils.RigidBodyMaterialCfg(
            friction_combine_mode="multiply",
            restitution_combine_mode="multiply",
            static_friction=1.0,
            dynamic_friction=1.0,
            restitution=0.0,
        ),
    )
    terrain = TerrainImporterCfg(
        prim_path="/World/ground",
        terrain_type="plane",
        collision_group=-1,
        physics_material=sim_utils.RigidBodyMaterialCfg(
            friction_combine_mode="multiply",
            restitution_combine_mode="multiply",
            static_friction=1.0,
            dynamic_friction=1.0,
            restitution=0.0,
        ),
        debug_vis=False,
    )

    # scene
    scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=4096, env_spacing=2.5, replicate_physics=True)

    # robot
    robot: ArticulationCfg = CRAZYFLIE_CFG.replace(prim_path="/World/envs/env_.*/Robot")
    thrust_to_weight = 1.9
    moment_scale = 0.01

    # reward scales
    lin_vel_reward_scale = -0.05
    ang_vel_reward_scale = -0.01
    distance_to_goal_reward_scale = 15.0


class QuadcopterEnv(DirectRLEnv):
    cfg: QuadcopterEnvCfg

    def __init__(self, cfg: QuadcopterEnvCfg, render_mode: str | None = None, **kwargs):
        super().__init__(cfg, render_mode, **kwargs)

        # Total thrust and moment applied to the base of the quadcopter
        self._actions = torch.zeros(self.num_envs, gym.spaces.flatdim(self.single_action_space), device=self.device)
        self._thrust = torch.zeros(self.num_envs, 1, 3, device=self.device)
        self._moment = torch.zeros(self.num_envs, 1, 3, device=self.device)
        # Goal position
        self._desired_pos_w = torch.zeros(self.num_envs, 3, device=self.device)

        # Logging
        self._episode_sums = {
            key: torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
            for key in [
                "lin_vel",
                "ang_vel",
                "distance_to_goal",
            ]
        }
        # Get specific body indices
        self._body_id = self._robot.find_bodies("body")[0]
        self._robot_mass = self._robot.root_physx_view.get_masses()[0].sum()
        self._gravity_magnitude = torch.tensor(self.sim.cfg.gravity, device=self.device).norm()
        self._robot_weight = (self._robot_mass * self._gravity_magnitude).item()

        # add handle for debug visualization (this is set to a valid handle inside set_debug_vis)
        self.set_debug_vis(self.cfg.debug_vis)

    def _setup_scene(self):
        self._robot = Articulation(self.cfg.robot)
        self.scene.articulations["robot"] = self._robot

        self.cfg.terrain.num_envs = self.scene.cfg.num_envs
        self.cfg.terrain.env_spacing = self.scene.cfg.env_spacing
        self._terrain = self.cfg.terrain.class_type(self.cfg.terrain)
        # clone, filter, and replicate
        self.scene.clone_environments(copy_from_source=False)
        self.scene.filter_collisions(global_prim_paths=[self.cfg.terrain.prim_path])
        # add lights
        light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75))
        light_cfg.func("/World/Light", light_cfg)

Dear
yoko

3

Thank you for your interest in Isaac Lab. To ensure efficient support and collaboration, please submit your topic to its GitHub repo following the instructions provided on Isaac Lab’s Contributing Guidelines regarding discussions, submitting issues, feature requests, and contributing to the project.

We appreciate your understanding and look forward to assisting you.

4

Hello, I’ve recorded my question on github Adding a wind field to crazyfly RL training code, waiting to hear back from you!

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