diff --git a/source/isaaclab/isaaclab/envs/mdp/events.py b/source/isaaclab/isaaclab/envs/mdp/events.py
index 0c23c902427..cb7b443d9c9 100644
--- a/source/isaaclab/isaaclab/envs/mdp/events.py
+++ b/source/isaaclab/isaaclab/envs/mdp/events.py
@@ -14,12 +14,14 @@
 
 from __future__ import annotations
 
+import math
 import torch
 from typing import TYPE_CHECKING, Literal
 
 import carb
 import omni.physics.tensors.impl.api as physx
 import omni.usd
+from isaacsim.core.utils.extensions import enable_extension
 from pxr import Gf, Sdf, UsdGeom, Vt
 
 import isaaclab.sim as sim_utils
@@ -274,6 +276,99 @@ def __call__(
         self.asset.root_physx_view.set_material_properties(materials, env_ids)
 
 
+class randomize_visual_texture_material(ManagerTermBase):
+    """Randomize the visual texture of bodies on an asset using Replicator API.
+
+    This function randomizes the visual texture of the bodies of the asset using the Replicator API.
+    The function samples random textures from the given texture paths and applies them to the bodies
+    of the asset. The textures are projected onto the bodies and rotated by the given angles.
+
+    .. note::
+        The function assumes that the asset follows the prim naming convention as:
+        "{asset_prim_path}/{body_name}/visuals" where the body name is the name of the body to
+        which the texture is applied. This is the default prim ordering when importing assets
+        from the asset converters in Isaac Lab.
+
+    .. note::
+        When randomizing the texture of individual assets, please make sure to set
+        :attr:`isaaclab.scene.InteractiveSceneCfg.replicate_physics` to False. This ensures that physics
+        parser will parse the individual asset properties separately.
+    """
+
+    def __init__(self, cfg: EventTermCfg, env: ManagerBasedEnv):
+        """Initialize the term.
+
+        Args:
+            cfg: The configuration of the event term.
+            env: The environment instance.
+        """
+        super().__init__(cfg, env)
+
+        enable_extension("omni.replicator.core")
+
+        # we import the module here since we may not always need the replicator
+        import omni.replicator.core as rep
+
+        # read parameters from the configuration
+        asset_cfg: SceneEntityCfg = cfg.params.get("asset_cfg")
+        texture_paths = cfg.params.get("texture_paths")
+        event_name = cfg.params.get("event_name")
+        texture_rotation = cfg.params.get("texture_rotation", (0.0, 0.0))
+
+        # check to make sure replicate_physics is set to False, else raise warning
+        if env.cfg.scene.replicate_physics:
+            raise ValueError(
+                "Unable to randomize visual texture material - ensure InteractiveSceneCfg's replicate_physics parameter"
+                " is set to False."
+            )
+
+        # convert from radians to degrees
+        texture_rotation = tuple(math.degrees(angle) for angle in texture_rotation)
+
+        # obtain the asset entity
+        asset_entity = env.scene[asset_cfg.name]
+        # join all bodies in the asset
+        body_names = asset_cfg.body_names
+        if isinstance(body_names, str):
+            body_names_regex = body_names
+        elif isinstance(body_names, list):
+            body_names_regex = "|".join(body_names)
+        else:
+            body_names_regex = ".*"
+
+        # Create the omni-graph node for the randomization term
+        def rep_texture_randomization():
+            prims_group = rep.get.prims(path_pattern=f"{asset_entity.cfg.prim_path}/{body_names_regex}/visuals")
+
+            with prims_group:
+                rep.randomizer.texture(
+                    textures=texture_paths, project_uvw=True, texture_rotate=rep.distribution.uniform(*texture_rotation)
+                )
+
+            return prims_group.node
+
+        # Register the event to the replicator
+        with rep.trigger.on_custom_event(event_name=event_name):
+            rep_texture_randomization()
+
+    def __call__(
+        self,
+        env: ManagerBasedEnv,
+        env_ids: torch.Tensor,
+        event_name: str,
+        asset_cfg: SceneEntityCfg,
+        texture_paths: list[str],
+        texture_rotation: tuple[float, float] = (0.0, 0.0),
+    ):
+        # import replicator
+        import omni.replicator.core as rep
+
+        # only send the event to the replicator
+        # note: This triggers the nodes for all the environments.
+        #   We need to investigate how to make it happen only for a subset based on env_ids.
+        rep.utils.send_og_event(event_name)
+
+
 def randomize_rigid_body_mass(
     env: ManagerBasedEnv,
     env_ids: torch.Tensor | None,
diff --git a/source/isaaclab/test/envs/test_scale_randomization.py b/source/isaaclab/test/envs/test_scale_randomization.py
new file mode 100644
index 00000000000..122f011f531
--- /dev/null
+++ b/source/isaaclab/test/envs/test_scale_randomization.py
@@ -0,0 +1,345 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""
+This script checks the functionality of scale randomization.
+"""
+
+from __future__ import annotations
+
+"""Launch Isaac Sim Simulator first."""
+
+from isaaclab.app import AppLauncher, run_tests
+
+# launch omniverse app
+app_launcher = AppLauncher(headless=True, enable_cameras=True)
+simulation_app = app_launcher.app
+
+"""Rest everything follows."""
+
+import torch
+import unittest
+
+import omni.usd
+from pxr import Sdf
+
+import isaaclab.envs.mdp as mdp
+import isaaclab.sim as sim_utils
+from isaaclab.assets import AssetBaseCfg, RigidObject, RigidObjectCfg
+from isaaclab.envs import ManagerBasedEnv, ManagerBasedEnvCfg
+from isaaclab.managers import ActionTerm, ActionTermCfg
+from isaaclab.managers import EventTermCfg as EventTerm
+from isaaclab.managers import ObservationGroupCfg as ObsGroup
+from isaaclab.managers import ObservationTermCfg as ObsTerm
+from isaaclab.managers import SceneEntityCfg
+from isaaclab.scene import InteractiveSceneCfg
+from isaaclab.terrains import TerrainImporterCfg
+from isaaclab.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 for scale randomization
+    cube1: RigidObjectCfg = RigidObjectCfg(
+        prim_path="{ENV_REGEX_NS}/cube1",
+        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)),
+    )
+
+    # add cube for static scale values
+    cube2: RigidObjectCfg = RigidObjectCfg(
+        prim_path="{ENV_REGEX_NS}/cube2",
+        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="cube1")
+
+
+@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("cube1")})
+
+        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("cube1"),
+        },
+    )
+
+    # Scale randomization as intended
+    randomize_cube1__scale = EventTerm(
+        func=mdp.randomize_rigid_body_scale,
+        mode="prestartup",
+        params={
+            "scale_range": {"x": (0.5, 1.5), "y": (0.5, 1.5), "z": (0.5, 1.5)},
+            "asset_cfg": SceneEntityCfg("cube1"),
+        },
+    )
+
+    # Static scale values
+    randomize_cube2__scale = EventTerm(
+        func=mdp.randomize_rigid_body_scale,
+        mode="prestartup",
+        params={
+            "scale_range": {"x": (1.0, 1.0), "y": (1.0, 1.0), "z": (1.0, 1.0)},
+            "asset_cfg": SceneEntityCfg("cube2"),
+        },
+    )
+
+
+##
+# Environment configuration
+##
+
+
+@configclass
+class CubeEnvCfg(ManagerBasedEnvCfg):
+    """Configuration for the locomotion velocity-tracking environment."""
+
+    # Scene settings
+    scene: MySceneCfg = MySceneCfg(num_envs=10, 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
+
+        stage = omni.usd.get_context().get_stage()
+
+        # test to make sure all assets in the scene are created
+        all_prim_paths = sim_utils.find_matching_prim_paths("/World/envs/env_.*/cube.*/.*")
+        self.assertEqual(len(all_prim_paths), (env.num_envs * 2))
+
+        # test to make sure randomized values are truly random
+        applied_scaling_randomization = set()
+        prim_paths = sim_utils.find_matching_prim_paths("/World/envs/env_.*/cube1")
+
+        for i in range(3):
+            prim_spec = Sdf.CreatePrimInLayer(stage.GetRootLayer(), prim_paths[i])
+            scale_spec = prim_spec.GetAttributeAtPath(prim_paths[i] + ".xformOp:scale")
+            if scale_spec.default in applied_scaling_randomization:
+                raise ValueError(
+                    "Detected repeat in applied scale values - indication scaling randomization is not working."
+                )
+            applied_scaling_randomization.add(scale_spec.default)
+
+        # test to make sure that fixed values are assigned correctly
+        prim_paths = sim_utils.find_matching_prim_paths("/World/envs/env_.*/cube2")
+        for i in range(3):
+            prim_spec = Sdf.CreatePrimInLayer(stage.GetRootLayer(), prim_paths[i])
+            scale_spec = prim_spec.GetAttributeAtPath(prim_paths[i] + ".xformOp:scale")
+            self.assertEqual(tuple(scale_spec.default), (1.0, 1.0, 1.0))
+
+        # simulate physics
+        with torch.inference_mode():
+            for count in range(200):
+                # reset every few steps to check nothing breaks
+                if count % 100 == 0:
+                    env.reset()
+                # step the environment
+                env.step(target_position)
+
+        env.close()
+
+    def test_scale_randomization_failure_replicate_physics(self):
+        with self.assertRaises(ValueError):
+            cfg_failure = CubeEnvCfg()
+            cfg_failure.scene.replicate_physics = True
+            env = ManagerBasedEnv(cfg_failure)
+
+        env.close()
+
+
+if __name__ == "__main__":
+    run_tests()
diff --git a/source/isaaclab/test/envs/test_texture_randomization.py b/source/isaaclab/test/envs/test_texture_randomization.py
new file mode 100644
index 00000000000..cab13757101
--- /dev/null
+++ b/source/isaaclab/test/envs/test_texture_randomization.py
@@ -0,0 +1,189 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""
+This script tests the functionality of texture randomization applied to the cartpole scene.
+"""
+
+"""Launch Isaac Sim Simulator first."""
+
+from isaaclab.app import AppLauncher, run_tests
+
+# launch omniverse app
+app_launcher = AppLauncher(headless=True, enable_cameras=True)
+simulation_app = app_launcher.app
+
+"""Rest everything follows."""
+
+import math
+import torch
+import unittest
+
+import isaaclab.envs.mdp as mdp
+from isaaclab.envs import ManagerBasedEnv, ManagerBasedEnvCfg
+from isaaclab.managers import EventTermCfg as EventTerm
+from isaaclab.managers import ObservationGroupCfg as ObsGroup
+from isaaclab.managers import ObservationTermCfg as ObsTerm
+from isaaclab.managers import SceneEntityCfg
+from isaaclab.utils import configclass
+from isaaclab.utils.assets import NVIDIA_NUCLEUS_DIR
+
+from isaaclab_tasks.manager_based.classic.cartpole.cartpole_env_cfg import CartpoleSceneCfg
+
+
+@configclass
+class ActionsCfg:
+    """Action specifications for the environment."""
+
+    joint_efforts = mdp.JointEffortActionCfg(asset_name="robot", joint_names=["slider_to_cart"], scale=5.0)
+
+
+@configclass
+class ObservationsCfg:
+    """Observation specifications for the environment."""
+
+    @configclass
+    class PolicyCfg(ObsGroup):
+        """Observations for policy group."""
+
+        # observation terms (order preserved)
+        joint_pos_rel = ObsTerm(func=mdp.joint_pos_rel)
+        joint_vel_rel = ObsTerm(func=mdp.joint_vel_rel)
+
+        def __post_init__(self) -> None:
+            self.enable_corruption = False
+            self.concatenate_terms = True
+
+    # observation groups
+    policy: PolicyCfg = PolicyCfg()
+
+
+@configclass
+class EventCfg:
+    """Configuration for events."""
+
+    # on reset apply a new set of textures
+    cart_texture_randomizer = EventTerm(
+        func=mdp.randomize_visual_texture_material,
+        mode="reset",
+        params={
+            "asset_cfg": SceneEntityCfg("robot", body_names=["cart"]),
+            "texture_paths": [
+                f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Bamboo_Planks/Bamboo_Planks_BaseColor.png",
+                f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Cherry/Cherry_BaseColor.png",
+                f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Oak/Oak_BaseColor.png",
+                f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Timber/Timber_BaseColor.png",
+                f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Timber_Cladding/Timber_Cladding_BaseColor.png",
+                f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Walnut_Planks/Walnut_Planks_BaseColor.png",
+            ],
+            "event_name": "cart_texture_randomizer",
+            "texture_rotation": (math.pi / 2, math.pi / 2),
+        },
+    )
+
+    pole_texture_randomizer = EventTerm(
+        func=mdp.randomize_visual_texture_material,
+        mode="reset",
+        params={
+            "asset_cfg": SceneEntityCfg("robot", body_names=["pole"]),
+            "texture_paths": [
+                f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Bamboo_Planks/Bamboo_Planks_BaseColor.png",
+                f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Cherry/Cherry_BaseColor.png",
+                f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Oak/Oak_BaseColor.png",
+                f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Timber/Timber_BaseColor.png",
+                f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Timber_Cladding/Timber_Cladding_BaseColor.png",
+                f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Wood/Walnut_Planks/Walnut_Planks_BaseColor.png",
+            ],
+            "event_name": "pole_texture_randomizer",
+            "texture_rotation": (math.pi / 2, math.pi / 2),
+        },
+    )
+
+    reset_cart_position = EventTerm(
+        func=mdp.reset_joints_by_offset,
+        mode="reset",
+        params={
+            "asset_cfg": SceneEntityCfg("robot", joint_names=["slider_to_cart"]),
+            "position_range": (-1.0, 1.0),
+            "velocity_range": (-0.1, 0.1),
+        },
+    )
+
+    reset_pole_position = EventTerm(
+        func=mdp.reset_joints_by_offset,
+        mode="reset",
+        params={
+            "asset_cfg": SceneEntityCfg("robot", joint_names=["cart_to_pole"]),
+            "position_range": (-0.125 * math.pi, 0.125 * math.pi),
+            "velocity_range": (-0.01 * math.pi, 0.01 * math.pi),
+        },
+    )
+
+
+@configclass
+class CartpoleEnvCfg(ManagerBasedEnvCfg):
+    """Configuration for the cartpole environment."""
+
+    # Scene settings
+    scene = CartpoleSceneCfg(env_spacing=2.5)
+
+    # Basic settings
+    actions = ActionsCfg()
+    observations = ObservationsCfg()
+    events = EventCfg()
+
+    def __post_init__(self):
+        """Post initialization."""
+        # viewer settings
+        self.viewer.eye = [4.5, 0.0, 6.0]
+        self.viewer.lookat = [0.0, 0.0, 2.0]
+        # step settings
+        self.decimation = 4  # env step every 4 sim steps: 200Hz / 4 = 50Hz
+        # simulation settings
+        self.sim.dt = 0.005  # sim step every 5ms: 200Hz
+
+
+class TestTextureRandomization(unittest.TestCase):
+    """Test for texture randomization"""
+
+    """
+    Tests
+    """
+
+    def test_texture_randomization(self):
+        # set the arguments
+        env_cfg = CartpoleEnvCfg()
+        env_cfg.scene.num_envs = 16
+        env_cfg.scene.replicate_physics = False
+
+        # setup base environment
+        env = ManagerBasedEnv(cfg=env_cfg)
+
+        # simulate physics
+        with torch.inference_mode():
+            for count in range(50):
+                # reset every few steps to check nothing breaks
+                if count % 10 == 0:
+                    env.reset()
+                # sample random actions
+                joint_efforts = torch.randn_like(env.action_manager.action)
+                # step the environment
+                env.step(joint_efforts)
+
+        env.close()
+
+    def test_texture_randomization_failure_replicate_physics(self):
+        with self.assertRaises(ValueError):
+            cfg_failure = CartpoleEnvCfg()
+            cfg_failure.scene.num_envs = 16
+            cfg_failure.scene.replicate_physics = True
+            env = ManagerBasedEnv(cfg_failure)
+
+        env.close()
+
+
+if __name__ == "__main__":
+    # run the main function
+    run_tests()