unfinished but created intial test for scale randomization

Author: hapatel-bdai

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/manager_based_env.py

@@ -363,7 +363,7 @@ def _apply_scene_randomization(self):
         applied_scene_randomization = False
         # iterate over all event terms
         for term_name, term_cfg in self.cfg.events.__dict__.items():
-            # check for non config
+            # check for none config
             if term_cfg is None:
                 continue
             # call event terms corresponding to the scene-level randomization

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/mdp/events.py

@@ -100,6 +100,13 @@ def randomize_scale(
     # extract the used quantities (to enable type-hinting)
     asset: RigidObject | Articulation = env.scene[asset_cfg.name]
 
+    # check to make sure replicate_physics is set to False, else raise warning
+    if env.cfg.scene.replicate_physics:
+        raise ValueError(
+            "Unable to randomize scale - ensure InteractiveSceneCfg's replicate_physics parameter"
+            " is set to False."
+        )
+
     # resolve environment ids
     if env_ids is None:
         env_ids = torch.arange(env.scene.num_envs, device="cpu")

source/extensions/omni.isaac.lab/test/envs/test_scale_randomization.py

@@ -0,0 +1,309 @@
+# Copyright (c) 2022-2024, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""
+This script creates a simple environment with a floating cube. The cube is controlled by a PD
+controller to track an arbitrary target position.
+
+While going through this tutorial, we recommend you to pay attention to how a custom action term
+is defined. The action term is responsible for processing the raw actions and applying them to the
+scene entities. The rest of the environment is similar to the previous tutorials.
+
+.. code-block:: bash
+
+    # Run the script
+    ./isaaclab.sh -p source/standalone/tutorials/03_envs/create_cube_base_env.py --num_envs 32
+"""
+
+from __future__ import annotations
+
+"""Launch Isaac Sim Simulator first."""
+
+from omni.isaac.lab.app import AppLauncher, run_tests
+
+# Can set this to False to see the GUI for debugging
+HEADLESS = False
+
+# launch omniverse app
+app_launcher = AppLauncher(headless=HEADLESS, enable_cameras=True)
+simulation_app = app_launcher.app
+
+"""Rest everything follows."""
+
+import torch
+import unittest
+
+import omni.isaac.lab.envs.mdp as mdp
+import omni.isaac.lab.sim as sim_utils
+from omni.isaac.lab.assets import AssetBaseCfg, RigidObject, RigidObjectCfg
+from omni.isaac.lab.envs import ManagerBasedEnv, ManagerBasedEnvCfg
+from omni.isaac.lab.managers import ActionTerm, ActionTermCfg
+from omni.isaac.lab.managers import EventTermCfg as EventTerm
+from omni.isaac.lab.managers import ObservationGroupCfg as ObsGroup
+from omni.isaac.lab.managers import ObservationTermCfg as ObsTerm
+from omni.isaac.lab.managers import SceneEntityCfg
+from omni.isaac.lab.scene import InteractiveSceneCfg
+from omni.isaac.lab.terrains import TerrainImporterCfg
+from omni.isaac.lab.utils import configclass
+
+##
+# Custom action term
+##
+
+
+class CubeActionTerm(ActionTerm):
+    """Simple action term that implements a PD controller to track a target position.
+
+    The action term is applied to the cube asset. It involves two steps:
+
+    1. **Process the raw actions**: Typically, this includes any transformations of the raw actions
+       that are required to map them to the desired space. This is called once per environment step.
+    2. **Apply the processed actions**: This step applies the processed actions to the asset.
+       It is called once per simulation step.
+
+    In this case, the action term simply applies the raw actions to the cube asset. The raw actions
+    are the desired target positions of the cube in the environment frame. The pre-processing step
+    simply copies the raw actions to the processed actions as no additional processing is required.
+    The processed actions are then applied to the cube asset by implementing a PD controller to
+    track the target position.
+    """
+
+    _asset: RigidObject
+    """The articulation asset on which the action term is applied."""
+
+    def __init__(self, cfg: CubeActionTermCfg, env: ManagerBasedEnv):
+        # call super constructor
+        super().__init__(cfg, env)
+        # create buffers
+        self._raw_actions = torch.zeros(env.num_envs, 3, device=self.device)
+        self._processed_actions = torch.zeros(env.num_envs, 3, device=self.device)
+        self._vel_command = torch.zeros(self.num_envs, 6, device=self.device)
+        # gains of controller
+        self.p_gain = cfg.p_gain
+        self.d_gain = cfg.d_gain
+
+    """
+    Properties.
+    """
+
+    @property
+    def action_dim(self) -> int:
+        return self._raw_actions.shape[1]
+
+    @property
+    def raw_actions(self) -> torch.Tensor:
+        return self._raw_actions
+
+    @property
+    def processed_actions(self) -> torch.Tensor:
+        return self._processed_actions
+
+    """
+    Operations
+    """
+
+    def process_actions(self, actions: torch.Tensor):
+        # store the raw actions
+        self._raw_actions[:] = actions
+        # no-processing of actions
+        self._processed_actions[:] = self._raw_actions[:]
+
+    def apply_actions(self):
+        # implement a PD controller to track the target position
+        pos_error = self._processed_actions - (self._asset.data.root_pos_w - self._env.scene.env_origins)
+        vel_error = -self._asset.data.root_lin_vel_w
+        # set velocity targets
+        self._vel_command[:, :3] = self.p_gain * pos_error + self.d_gain * vel_error
+        self._asset.write_root_velocity_to_sim(self._vel_command)
+
+
+@configclass
+class CubeActionTermCfg(ActionTermCfg):
+    """Configuration for the cube action term."""
+
+    class_type: type = CubeActionTerm
+    """The class corresponding to the action term."""
+
+    p_gain: float = 5.0
+    """Proportional gain of the PD controller."""
+    d_gain: float = 0.5
+    """Derivative gain of the PD controller."""
+
+
+##
+# Custom observation term
+##
+
+
+def base_position(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg) -> torch.Tensor:
+    """Root linear velocity in the asset's root frame."""
+    # extract the used quantities (to enable type-hinting)
+    asset: RigidObject = env.scene[asset_cfg.name]
+    return asset.data.root_pos_w - env.scene.env_origins
+
+
+##
+# Scene definition
+##
+
+
+@configclass
+class MySceneCfg(InteractiveSceneCfg):
+    """Example scene configuration.
+
+    The scene comprises of a ground plane, light source and floating cubes (gravity disabled).
+    """
+
+    # add terrain
+    terrain = TerrainImporterCfg(prim_path="/World/ground", terrain_type="plane", debug_vis=False)
+
+    # add cube
+    cube: RigidObjectCfg = RigidObjectCfg(
+        prim_path="{ENV_REGEX_NS}/cube",
+        spawn=sim_utils.CuboidCfg(
+            size=(0.2, 0.2, 0.2),
+            rigid_props=sim_utils.RigidBodyPropertiesCfg(max_depenetration_velocity=1.0, disable_gravity=True),
+            mass_props=sim_utils.MassPropertiesCfg(mass=1.0),
+            physics_material=sim_utils.RigidBodyMaterialCfg(),
+            visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 0.0)),
+        ),
+        init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, 0.0, 5)),
+    )
+
+    # lights
+    light = AssetBaseCfg(
+        prim_path="/World/light",
+        spawn=sim_utils.DistantLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0),
+    )
+
+
+##
+# Environment settings
+##
+
+
+@configclass
+class ActionsCfg:
+    """Action specifications for the MDP."""
+
+    joint_pos = CubeActionTermCfg(asset_name="cube")
+
+
+@configclass
+class ObservationsCfg:
+    """Observation specifications for the MDP."""
+
+    @configclass
+    class PolicyCfg(ObsGroup):
+        """Observations for policy group."""
+
+        # cube velocity
+        position = ObsTerm(func=base_position, params={"asset_cfg": SceneEntityCfg("cube")})
+
+        def __post_init__(self):
+            self.enable_corruption = True
+            self.concatenate_terms = True
+
+    # observation groups
+    policy: PolicyCfg = PolicyCfg()
+
+
+@configclass
+class EventCfg:
+    """Configuration for events."""
+
+    reset_base = EventTerm(
+        func=mdp.reset_root_state_uniform,
+        mode="reset",
+        params={
+            "pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)},
+            "velocity_range": {
+                "x": (-0.5, 0.5),
+                "y": (-0.5, 0.5),
+                "z": (-0.5, 0.5),
+            },
+            "asset_cfg": SceneEntityCfg("cube"),
+        },
+    )
+
+    randomize_scale = EventTerm(
+        func=mdp.randomize_scale,
+        mode="scene",
+        params={
+            "scale_range": {"x": (0.5, 1.5), "y": (0.5, 1.5), "z": (0.5, 1.5)},
+            "asset_cfg": SceneEntityCfg("cube"),
+        },
+    )
+
+
+##
+# Environment configuration
+##
+
+
+@configclass
+class CubeEnvCfg(ManagerBasedEnvCfg):
+    """Configuration for the locomotion velocity-tracking environment."""
+
+    # Scene settings
+    scene: MySceneCfg = MySceneCfg(num_envs=64, env_spacing=2.5, replicate_physics=False)
+    # Basic settings
+    observations: ObservationsCfg = ObservationsCfg()
+    actions: ActionsCfg = ActionsCfg()
+    events: EventCfg = EventCfg()
+
+    def __post_init__(self):
+        """Post initialization."""
+        # general settings
+        self.decimation = 2
+        # simulation settings
+        self.sim.dt = 0.01
+        self.sim.physics_material = self.scene.terrain.physics_material
+
+
+class TestScaleRandomization(unittest.TestCase):
+    """Test for texture randomization"""
+
+    """
+    Tests
+    """
+
+    def test_scale_randomization(self):
+        """Main function."""
+
+        # setup base environment
+        env = ManagerBasedEnv(cfg=CubeEnvCfg())
+        # setup target position commands
+        target_position = torch.rand(env.num_envs, 3, device=env.device) * 2
+        target_position[:, 2] += 2.0
+        # offset all targets so that they move to the world origin
+        target_position -= env.scene.env_origins
+
+        # simulate physics
+        count = 0
+        obs, _ = env.reset()
+        while simulation_app.is_running():
+            with torch.inference_mode():
+                # reset
+                if count % 300 == 0:
+                    count = 0
+                    obs, _ = env.reset()
+                    print("-" * 80)
+                    print("[INFO]: Resetting environment...")
+                # step env
+                obs, _ = env.step(target_position)
+                # print mean squared position error between target and current position
+                error = torch.norm(obs["policy"] - target_position).mean().item()
+                print(f"[Step: {count:04d}]: Mean position error: {error:.4f}")
+                # update counter
+                count += 1
+
+
+if __name__ == "__main__":
+    run_tests()
+    # # run the main function
+    # main()
+    # # close sim app
+    # simulation_app.close()