> This page is for version Release 3.0 (default). > For other versions, use one of these documentation indexes: > - Release 3.0 (default): https://docs.nvidia.com/isaaclab/3.0/llms.txt > - Release 2.3: https://docs.nvidia.com/isaaclab/2.3/llms.txt > - Release 2.2: https://docs.nvidia.com/isaaclab/2.2/llms.txt > - Release 2.1: https://docs.nvidia.com/isaaclab/2.1/llms.txt > For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/isaaclab/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/isaaclab/_mcp/server. # Creating a Manager-Based Base Environment > **Info** > > This tutorial is the source of truth for the `isaaclab-randomizing-with-events` agent skill > ([skills/user/domain-randomization-events/](https://github.com/isaac-sim/IsaacLab/blob/release/3.0.0/skills/user/domain-randomization-events/SKILL.md)). > When you change this page, update the skill so agent guidance stays in sync. See > [/source/developer-tools/agent_skills](/source/developer-tools/agent_skills). Environments bring together different aspects of the simulation such as the scene, observations and actions spaces, reset events etc. to create a coherent interface for various applications. In Isaac Lab, manager-based environments are implemented as `envs.ManagerBasedEnv` and `envs.ManagerBasedRLEnv` classes. The two classes are very similar, but `envs.ManagerBasedRLEnv` is useful for reinforcement learning tasks and contains rewards, terminations, curriculum and command generation. The `envs.ManagerBasedEnv` class is useful for traditional robot control and doesn't contain rewards and terminations. In this tutorial, we will look at the base class `envs.ManagerBasedEnv` and its corresponding configuration class `envs.ManagerBasedEnvCfg` for the manager-based workflow. We will use the cartpole environment from earlier to illustrate the different components in creating a new `envs.ManagerBasedEnv` environment. ## The Code The tutorial corresponds to the `create_cartpole_base_env` script in the `scripts/tutorials/03_envs` directory. ```python # Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause """ This script demonstrates how to create a simple environment with a cartpole. It combines the concepts of scene, action, observation and event managers to create an environment. ~~~bash uv run python scripts/tutorials/03_envs/create_cartpole_base_env.py --num_envs 32 ~~~ """ """Parse the command-line arguments first.""" import argparse from isaaclab.app import add_launcher_args, launch_simulation # add argparse arguments parser = argparse.ArgumentParser(description="Tutorial on creating a cartpole base environment.") parser.add_argument("--num_envs", type=int, default=16, help="Number of environments to spawn.") # append simulation launcher cli args add_launcher_args(parser) # parse the arguments args_cli = parser.parse_args() """Rest everything follows.""" import math import torch import isaaclab.envs.mdp as mdp from isaaclab.envs import 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, instantiate from isaaclab.visualizers import VisualizerCfg from isaaclab_tasks.core.cartpole.cartpole_manager_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 startup add_pole_mass = EventTerm( func=mdp.randomize_rigid_body_mass, mode="startup", params={ "asset_cfg": SceneEntityCfg("robot", body_names=["pole"]), "mass_distribution_params": (0.1, 0.5), "operation": "add", }, ) # on reset 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(num_envs=1024, env_spacing=2.5) # Basic settings observations = ObservationsCfg() actions = ActionsCfg() events = EventCfg() def __post_init__(self): """Post initialization.""" # viewer settings self.sim.default_visualizer_cfg = VisualizerCfg(eye=(4.5, 0.0, 6.0), 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 def main(): """Main function.""" # parse the arguments env_cfg = CartpoleEnvCfg() env_cfg.scene.num_envs = args_cli.num_envs env_cfg.sim.device = args_cli.device # Launch the simulator runtime that the configuration needs with launch_simulation(env_cfg, args_cli): # setup base environment env = instantiate(env_cfg) # simulate physics count = 0 while env.sim.is_running(): with torch.inference_mode(): # reset if count % 300 == 0: count = 0 env.reset() print("-" * 80) print("[INFO]: Resetting environment...") # sample random actions joint_efforts = torch.randn_like(env.action_manager.action) # step the environment obs, _ = env.step(joint_efforts) # print current orientation of pole print("[Env 0]: Pole joint: ", obs["policy"][0][1].item()) # update counter count += 1 # close the environment env.close() if __name__ == "__main__": # run the main function main() ``` ## The Code Explained The base class `envs.ManagerBasedEnv` wraps around many intricacies of the simulation interaction and provides a simple interface for the user to run the simulation and interact with it. It is composed of the following components: * `scene.InteractiveScene` - The scene that is used for the simulation. * `managers.ActionManager` - The manager that handles actions. * `managers.ObservationManager` - The manager that handles observations. * `managers.EventManager` - The manager that schedules operations (such as domain randomization) at specified simulation events. For instance, at startup, on resets, or periodic intervals. By configuring these components, the user can create different variations of the same environment with minimal effort. In this tutorial, we will go through the different components of the `envs.ManagerBasedEnv` class and how to configure them to create a new environment. ### Designing the scene The first step in creating a new environment is to configure its scene. For the cartpole environment, we will be using the scene from the previous tutorial. Thus, we omit the scene configuration here. For more details on how to configure a scene, see [Using the Interactive Scene](/source/how-to/create_scene). ### Defining actions In the previous tutorial, we directly input the action to the cartpole using `robot.actuators.target_command.set_effort_index`. In this tutorial, we will use the `managers.ActionManager` to handle the actions. The action manager can comprise of multiple `managers.ActionTerm`. Each action term is responsible for applying *control* over a specific aspect of the environment. For instance, for robotic arm, we can have two action terms -- one for controlling the joints of the arm, and the other for controlling the gripper. This composition allows the user to define different control schemes for different aspects of the environment. In the cartpole environment, we want to control the force applied to the cart to balance the pole. Thus, we will create an action term that controls the force applied to the cart. ```python @configclass class ActionsCfg: """Action specifications for the environment.""" joint_efforts = mdp.JointEffortActionCfg(asset_name="robot", joint_names=["slider_to_cart"], scale=5.0) ``` ### Defining observations While the scene defines the state of the environment, the observations define the states that are observable by the agent. These observations are used by the agent to make decisions on what actions to take. In Isaac Lab, the observations are computed by the `managers.ObservationManager` class. Similar to the action manager, the observation manager can comprise of multiple observation terms. These are further grouped into observation groups which are used to define different observation spaces for the environment. For instance, for hierarchical control, we may want to define two observation groups -- one for the low level controller and the other for the high level controller. It is assumed that all the observation terms in a group have the same dimensions. For this tutorial, we will only define one observation group named `"policy"`. While not completely prescriptive, this group is a necessary requirement for various wrappers in Isaac Lab. We define a group by inheriting from the `managers.ObservationGroupCfg` class. This class collects different observation terms and help define common properties for the group, such as enabling noise corruption or concatenating the observations into a single tensor. The individual terms are defined by inheriting from the `managers.ObservationTermCfg` class. This class takes in the `managers.ObservationTermCfg.func` that specifies the function or callable class that computes the observation for that term. It includes other parameters for defining the noise model, clipping, scaling, etc. However, we leave these parameters to their default values for this tutorial. ```python @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() ``` For a policy that needs a flattened history with all terms from each time step together, set `history_length` and `history_order="time"` on its `managers.ObservationGroupCfg`. The resulting observation has shape `(num_envs, history_length * combined_term_dim)` and can be reshaped to `(num_envs, history_length, combined_term_dim)`. The default `"term"` order keeps each term's history together; use it for policies trained with the existing layout. ### Defining events At this point, we have defined the scene, actions and observations for the cartpole environment. The general idea for all these components is to define the configuration classes and then pass them to the corresponding managers. The event manager is no different. The `managers.EventManager` class is responsible for events corresponding to changes in the simulation state. This includes resetting (or randomizing) the scene, randomizing physical properties (such as mass, friction, etc.), and varying visual properties (such as colors, textures, etc.). Each of these are specified through the `managers.EventTermCfg` class, which takes in the `managers.EventTermCfg.func` that specifies the function or callable class that performs the event. Additionally, it expects the **mode** of the event. The mode specifies when the event term should be applied. It is possible to specify your own mode. For this, you'll need to adapt the `~envs.ManagerBasedEnv` class. However, out of the box, Isaac Lab provides three commonly used modes: * `"startup"` - Event that takes place only once at environment startup. * `"reset"` - Event that occurs on environment termination and reset. * `"interval"` - Event that are executed at a given interval, i.e., periodically after a certain number of steps. For this example, we define events that randomize the pole's mass on startup. This is done only once since this operation is expensive and we don't want to do it on every reset. We also create an event to randomize the initial joint state of the cartpole and the pole at every reset. ```python @configclass class EventCfg: """Configuration for events.""" # on startup add_pole_mass = EventTerm( func=mdp.randomize_rigid_body_mass, mode="startup", params={ "asset_cfg": SceneEntityCfg("robot", body_names=["pole"]), "mass_distribution_params": (0.1, 0.5), "operation": "add", }, ) # on reset 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), }, ) ``` ### Tying it all together Having defined the scene and manager configurations, we can now define the environment configuration through the `envs.ManagerBasedEnvCfg` class. This class takes in the scene, action, observation and event configurations. In addition to these, it also takes in the `envs.ManagerBasedEnvCfg.sim` which defines the simulation parameters such as the timestep, gravity, etc. This is initialized to the default values, but can be modified as needed. We recommend doing so by defining the `__post_init__` method in the `envs.ManagerBasedEnvCfg` class, which is called after the configuration is initialized. ```python @configclass class CartpoleEnvCfg(ManagerBasedEnvCfg): """Configuration for the cartpole environment.""" # Scene settings scene = CartpoleSceneCfg(num_envs=1024, env_spacing=2.5) # Basic settings observations = ObservationsCfg() actions = ActionsCfg() events = EventCfg() def __post_init__(self): """Post initialization.""" # viewer settings self.sim.default_visualizer_cfg = VisualizerCfg(eye=(4.5, 0.0, 6.0), 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 ``` ### Running the simulation Lastly, we revisit the simulation execution loop. This is now much simpler since we have abstracted away most of the details into the environment configuration. We only need to call the `envs.ManagerBasedEnv.reset` method to reset the environment and `envs.ManagerBasedEnv.step` method to step the environment. Both these functions return the observation and an info dictionary which may contain additional information provided by the environment. These can be used by an agent for decision-making. Before the environment is created, its configuration is passed to `app.launch_simulation`, which launches the simulator runtime that the configuration needs and closes it when the `with` block exits. Inside this block, `~isaaclab.utils.instantiate` constructs the environment from the class named by the configuration's `envs.ManagerBasedEnvCfg.class_type` attribute. The class is resolved lazily, so the environment class, which works on the USD stage, is only imported once the simulator runtime is running. The `envs.ManagerBasedEnv` class does not have any notion of terminations since that concept is specific for episodic tasks. Thus, the user is responsible for defining the termination condition for the environment. In this tutorial, we reset the simulation at regular intervals. ```python def main(): """Main function.""" # parse the arguments env_cfg = CartpoleEnvCfg() env_cfg.scene.num_envs = args_cli.num_envs env_cfg.sim.device = args_cli.device # Launch the simulator runtime that the configuration needs with launch_simulation(env_cfg, args_cli): # setup base environment env = instantiate(env_cfg) # simulate physics count = 0 while env.sim.is_running(): with torch.inference_mode(): # reset if count % 300 == 0: count = 0 env.reset() print("-" * 80) print("[INFO]: Resetting environment...") # sample random actions joint_efforts = torch.randn_like(env.action_manager.action) # step the environment obs, _ = env.step(joint_efforts) # print current orientation of pole print("[Env 0]: Pole joint: ", obs["policy"][0][1].item()) # update counter count += 1 # close the environment env.close() ``` An important thing to note above is that the entire simulation loop is wrapped inside the `torch.inference_mode` context manager. This is because the environment uses PyTorch operations under-the-hood and we want to ensure that the simulation is not slowed down by the overhead of PyTorch's autograd engine and gradients are not computed for the simulation operations. ## The Code Execution To run the base environment made in this tutorial, you can use the following command: #### uv (Recommended) ```bash uv run python scripts/tutorials/03_envs/create_cartpole_base_env.py --num_envs 32 --viz kit ``` #### isaaclab.sh / isaaclab.bat ```bash ./isaaclab.sh -p scripts/tutorials/03_envs/create_cartpole_base_env.py --num_envs 32 --viz kit ``` This should open a stage with a ground plane, light source, and cartpoles. The simulation should be playing with random actions on the cartpole. Additionally, it opens a UI window on the bottom right corner of the screen named `"Isaac Lab"`. This window contains different UI elements that can be used for debugging and visualization. ![result of create\_cartpole\_base\_env.py](/isaaclab/_fern-img/542f184bac0597be137e0a1b49796321823650f97c0f84ed731eb2529bb9d95d.webp) To stop the simulation, you can either close the window, or press `Ctrl+C` in the terminal where you started the simulation. In this tutorial, we learned about the different managers that help define a base environment. We include more examples of defining the base environment in the `scripts/tutorials/03_envs` directory. For completeness, they can be run using the following commands: #### uv (Recommended) ```bash # Floating cube environment with custom action term for PD control uv run python scripts/tutorials/03_envs/create_cube_base_env.py --num_envs 32 --viz kit # Quadrupedal locomotion environment with a policy that interacts with the environment uv run python scripts/tutorials/03_envs/create_quadruped_base_env.py --num_envs 32 --viz kit ``` #### isaaclab.sh / isaaclab.bat ```bash # Floating cube environment with custom action term for PD control ./isaaclab.sh -p scripts/tutorials/03_envs/create_cube_base_env.py --num_envs 32 --viz kit # Quadrupedal locomotion environment with a policy that interacts with the environment ./isaaclab.sh -p scripts/tutorials/03_envs/create_quadruped_base_env.py --num_envs 32 --viz kit ``` In the following tutorial, we will look at the `envs.ManagerBasedRLEnv` class and how to use it to create a Markovian Decision Process (MDP).