Managed environments actions / observations descriptions (#2730)

# Description

Experimental branch to generate observations and actions descriptions
from managed environments.

## Type of change

- New feature (non-breaking change which adds functionality)
- This change requires a documentation update

## Checklist

- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [x] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [ ] I have updated the changelog and the corresponding version in the
extension's `config/extension.toml` file
- [x] I have added my name to the `CONTRIBUTORS.md` or my name already
exists there

---------
Signed-off-by: ooctipus <zhengyuz@nvidia.com>
Co-authored-by: ooctipus <zhengyuz@nvidia.com>

docs/source/_static/policy_deployment/01_io_descriptors/isaac_velocity_flat_anymal_d_v0_IO_descriptors.yaml

+# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+actions:
+- action_type: JointAction
+  clip: null
+  dtype: torch.float32
+  extras:
+    description: Joint action term that applies the processed actions to the articulation's
+      joints as position commands.
+  full_path: isaaclab.envs.mdp.actions.joint_actions.JointPositionAction
+  joint_names:
+  - LF_HAA
+  - LH_HAA
+  - RF_HAA
+  - RH_HAA
+  - LF_HFE
+  - LH_HFE
+  - RF_HFE
+  - RH_HFE
+  - LF_KFE
+  - LH_KFE
+  - RF_KFE
+  - RH_KFE
+  mdp_type: Action
+  name: joint_position_action
+  offset:
+  - 0.0
+  - 0.0
+  - 0.0
+  - 0.0
+  - 0.4000000059604645
+  - -0.4000000059604645
+  - 0.4000000059604645
+  - -0.4000000059604645
+  - -0.800000011920929
+  - 0.800000011920929
+  - -0.800000011920929
+  - 0.800000011920929
+  scale: 0.5
+  shape:
+  - 12
+articulations:
+  robot:
+    default_joint_armature:
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    default_joint_damping:
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    default_joint_friction:
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    default_joint_pos:
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.4000000059604645
+    - -0.4000000059604645
+    - 0.4000000059604645
+    - -0.4000000059604645
+    - -0.800000011920929
+    - 0.800000011920929
+    - -0.800000011920929
+    - 0.800000011920929
+    default_joint_pos_limits:
+    - - -0.7853984236717224
+      - 0.6108654141426086
+    - - -0.7853984236717224
+      - 0.6108654141426086
+    - - -0.6108654141426086
+      - 0.7853984236717224
+    - - -0.6108654141426086
+      - 0.7853984236717224
+    - - -9.42477798461914
+      - 9.42477798461914
+    - - -9.42477798461914
+      - 9.42477798461914
+    - - -9.42477798461914
+      - 9.42477798461914
+    - - -9.42477798461914
+      - 9.42477798461914
+    - - -9.42477798461914
+      - 9.42477798461914
+    - - -9.42477798461914
+      - 9.42477798461914
+    - - -9.42477798461914
+      - 9.42477798461914
+    - - -9.42477798461914
+      - 9.42477798461914
+    default_joint_stiffness:
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    default_joint_vel:
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    joint_names:
+    - LF_HAA
+    - LH_HAA
+    - RF_HAA
+    - RH_HAA
+    - LF_HFE
+    - LH_HFE
+    - RF_HFE
+    - RH_HFE
+    - LF_KFE
+    - LH_KFE
+    - RF_KFE
+    - RH_KFE
+observations:
+  policy:
+  - dtype: torch.float32
+    extras:
+      axes:
+      - X
+      - Y
+      - Z
+      description: Root linear velocity in the asset's root frame.
+      modifiers: null
+      units: m/s
+    full_path: isaaclab.envs.mdp.observations.base_lin_vel
+    mdp_type: Observation
+    name: base_lin_vel
+    observation_type: RootState
+    overloads:
+      clip: null
+      flatten_history_dim: true
+      history_length: 0
+      scale: null
+    shape:
+    - 3
+  - dtype: torch.float32
+    extras:
+      axes:
+      - X
+      - Y
+      - Z
+      description: Root angular velocity in the asset's root frame.
+      modifiers: null
+      units: rad/s
+    full_path: isaaclab.envs.mdp.observations.base_ang_vel
+    mdp_type: Observation
+    name: base_ang_vel
+    observation_type: RootState
+    overloads:
+      clip: null
+      flatten_history_dim: true
+      history_length: 0
+      scale: null
+    shape:
+    - 3
+  - dtype: torch.float32
+    extras:
+      axes:
+      - X
+      - Y
+      - Z
+      description: Gravity projection on the asset's root frame.
+      modifiers: null
+      units: m/s^2
+    full_path: isaaclab.envs.mdp.observations.projected_gravity
+    mdp_type: Observation
+    name: projected_gravity
+    observation_type: RootState
+    overloads:
+      clip: null
+      flatten_history_dim: true
+      history_length: 0
+      scale: null
+    shape:
+    - 3
+  - dtype: torch.float32
+    extras:
+      description: The generated command from command term in the command manager
+        with the given name.
+      modifiers: null
+    full_path: isaaclab.envs.mdp.observations.generated_commands
+    mdp_type: Observation
+    name: generated_commands
+    observation_type: Command
+    overloads:
+      clip: null
+      flatten_history_dim: true
+      history_length: 0
+      scale: null
+    shape:
+    - 3
+  - dtype: torch.float32
+    extras:
+      description: 'The joint positions of the asset w.r.t. the default joint positions.
+        Note: Only the joints configured in :attr:`asset_cfg.joint_ids` will have
+        their positions returned.'
+      modifiers: null
+      units: rad
+    full_path: isaaclab.envs.mdp.observations.joint_pos_rel
+    joint_names:
+    - LF_HAA
+    - LH_HAA
+    - RF_HAA
+    - RH_HAA
+    - LF_HFE
+    - LH_HFE
+    - RF_HFE
+    - RH_HFE
+    - LF_KFE
+    - LH_KFE
+    - RF_KFE
+    - RH_KFE
+    joint_pos_offsets:
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.4000000059604645
+    - -0.4000000059604645
+    - 0.4000000059604645
+    - -0.4000000059604645
+    - -0.800000011920929
+    - 0.800000011920929
+    - -0.800000011920929
+    - 0.800000011920929
+    mdp_type: Observation
+    name: joint_pos_rel
+    observation_type: JointState
+    overloads:
+      clip: null
+      flatten_history_dim: true
+      history_length: 0
+      scale: null
+    shape:
+    - 12
+  - dtype: torch.float32
+    extras:
+      description: 'The joint velocities of the asset w.r.t. the default joint velocities.
+        Note: Only the joints configured in :attr:`asset_cfg.joint_ids` will have
+        their velocities returned.'
+      modifiers: null
+      units: rad/s
+    full_path: isaaclab.envs.mdp.observations.joint_vel_rel
+    joint_names:
+    - LF_HAA
+    - LH_HAA
+    - RF_HAA
+    - RH_HAA
+    - LF_HFE
+    - LH_HFE
+    - RF_HFE
+    - RH_HFE
+    - LF_KFE
+    - LH_KFE
+    - RF_KFE
+    - RH_KFE
+    joint_vel_offsets:
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    mdp_type: Observation
+    name: joint_vel_rel
+    observation_type: JointState
+    overloads:
+      clip: null
+      flatten_history_dim: true
+      history_length: 0
+      scale: null
+    shape:
+    - 12
+  - dtype: torch.float32
+    extras:
+      description: The last input action to the environment. The name of the action
+        term for which the action is required. If None, the entire action tensor is
+        returned.
+      modifiers: null
+    full_path: isaaclab.envs.mdp.observations.last_action
+    mdp_type: Observation
+    name: last_action
+    observation_type: Action
+    overloads:
+      clip: null
+      flatten_history_dim: true
+      history_length: 0
+      scale: null
+    shape:
+    - 12
+scene:
+  decimation: 4
+  dt: 0.02
+  physics_dt: 0.005

docs/source/policy_deployment/index.rst

@@ -9,3 +9,4 @@ Below, you’ll find detailed examples of various policies for training and depl
     :maxdepth: 1
 
     00_hover/hover_policy
+    01_io_descriptors/io_descriptors_101

scripts/environments/export_IODescriptors.py

+# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""Script to an environment with random action agent."""
+
+"""Launch Isaac Sim Simulator first."""
+
+import argparse
+import os
+
+from isaaclab.app import AppLauncher
+
+# add argparse arguments
+parser = argparse.ArgumentParser(description="Random agent for Isaac Lab environments.")
+parser.add_argument("--task", type=str, default=None, help="Name of the task.")
+parser.add_argument("--output_dir", type=str, default=None, help="Path to the output directory.")
+# append AppLauncher cli args
+AppLauncher.add_app_launcher_args(parser)
+# parse the arguments
+args_cli = parser.parse_args()
+args_cli.headless = True
+
+# launch omniverse app
+app_launcher = AppLauncher(args_cli)
+simulation_app = app_launcher.app
+
+"""Rest everything follows."""
+
+import gymnasium as gym
+import torch
+
+import isaaclab_tasks  # noqa: F401
+from isaaclab_tasks.utils import parse_env_cfg
+
+# PLACEHOLDER: Extension template (do not remove this comment)
+
+
+def main():
+    """Random actions agent with Isaac Lab environment."""
+    # create environment configuration
+    env_cfg = parse_env_cfg(args_cli.task, device=args_cli.device, num_envs=1, use_fabric=True)
+    # create environment
+    env = gym.make(args_cli.task, cfg=env_cfg)
+
+    # print info (this is vectorized environment)
+    print(f"[INFO]: Gym observation space: {env.observation_space}")
+    print(f"[INFO]: Gym action space: {env.action_space}")
+    # reset environment
+    env.reset()
+
+    outs = env.unwrapped.get_IO_descriptors
+    out_observations = outs["observations"]
+    out_actions = outs["actions"]
+    out_articulations = outs["articulations"]
+    out_scene = outs["scene"]
+    # Make a yaml file with the output
+    import yaml
+
+    name = args_cli.task.lower().replace("-", "_")
+    name = name.replace(" ", "_")
+
+    if not os.path.exists(args_cli.output_dir):
+        os.makedirs(args_cli.output_dir)
+
+    with open(os.path.join(args_cli.output_dir, f"{name}_IO_descriptors.yaml"), "w") as f:
+        print(f"[INFO]: Exporting IO descriptors to {os.path.join(args_cli.output_dir, f'{name}_IO_descriptors.yaml')}")
+        yaml.safe_dump(outs, f)
+
+    for k in out_actions:
+        print(f"--- Action term: {k['name']} ---")
+        k.pop("name")
+        for k1, v1 in k.items():
+            print(f"{k1}: {v1}")
+
+    for obs_group_name, obs_group in out_observations.items():
+        print(f"--- Obs group: {obs_group_name} ---")
+        for k in obs_group:
+            print(f"--- Obs term: {k['name']} ---")
+            k.pop("name")
+            for k1, v1 in k.items():
+                print(f"{k1}: {v1}")
+
+    for articulation_name, articulation_data in out_articulations.items():
+        print(f"--- Articulation: {articulation_name} ---")
+        for k1, v1 in articulation_data.items():
+            print(f"{k1}: {v1}")
+
+    for k1, v1 in out_scene.items():
+        print(f"{k1}: {v1}")
+
+    env.step(torch.zeros(env.action_space.shape, device=env.unwrapped.device))
+    env.close()
+
+
+if __name__ == "__main__":
+    # run the main function
+    main()
+    # close sim app
+    simulation_app.close()

scripts/reinforcement_learning/rl_games/train.py

@@ -41,6 +41,7 @@ parser.add_argument(
     const=True,
     help="if toggled, this experiment will be tracked with Weights and Biases",
 )
+parser.add_argument("--export_io_descriptors", action="store_true", default=False, help="Export IO descriptors.")
 # append AppLauncher cli args
 AppLauncher.add_app_launcher_args(parser)
 # parse the arguments
@@ -64,6 +65,7 @@ import os
 import random
 from datetime import datetime
 
+import omni
 from rl_games.common import env_configurations, vecenv
 from rl_games.common.algo_observer import IsaacAlgoObserver
 from rl_games.torch_runner import Runner
@@ -147,6 +149,15 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
     clip_obs = agent_cfg["params"]["env"].get("clip_observations", math.inf)
     clip_actions = agent_cfg["params"]["env"].get("clip_actions", math.inf)
 
+    # set the IO descriptors output directory if requested
+    if isinstance(env_cfg, ManagerBasedRLEnvCfg):
+        env_cfg.export_io_descriptors = args_cli.export_io_descriptors
+        env_cfg.io_descriptors_output_dir = os.path.join(log_root_path, log_dir)
+    else:
+        omni.log.warn(
+            "IO descriptors are only supported for manager based RL environments. No IO descriptors will be exported."
+        )
+
     # create isaac environment
     env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None)
 

scripts/reinforcement_learning/rsl_rl/train.py

@@ -31,6 +31,7 @@ parser.add_argument("--max_iterations", type=int, default=None, help="RL Policy
 parser.add_argument(
     "--distributed", action="store_true", default=False, help="Run training with multiple GPUs or nodes."
 )
+parser.add_argument("--export_io_descriptors", action="store_true", default=False, help="Export IO descriptors.")
 # append RSL-RL cli arguments
 cli_args.add_rsl_rl_args(parser)
 # append AppLauncher cli args
@@ -77,6 +78,7 @@ import os
 import torch
 from datetime import datetime
 
+import omni
 from rsl_rl.runners import OnPolicyRunner
 
 from isaaclab.envs import (
@@ -140,6 +142,15 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
         log_dir += f"_{agent_cfg.run_name}"
     log_dir = os.path.join(log_root_path, log_dir)
 
+    # set the IO descriptors output directory if requested
+    if isinstance(env_cfg, ManagerBasedRLEnvCfg):
+        env_cfg.export_io_descriptors = args_cli.export_io_descriptors
+        env_cfg.io_descriptors_output_dir = log_dir
+    else:
+        omni.log.warn(
+            "IO descriptors are only supported for manager based RL environments. No IO descriptors will be exported."
+        )
+
     # create isaac environment
     env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None)
 

scripts/reinforcement_learning/sb3/train.py

@@ -30,6 +30,7 @@ parser.add_argument("--seed", type=int, default=None, help="Seed used for the en
 parser.add_argument("--log_interval", type=int, default=100_000, help="Log data every n timesteps.")
 parser.add_argument("--checkpoint", type=str, default=None, help="Continue the training from checkpoint.")
 parser.add_argument("--max_iterations", type=int, default=None, help="RL Policy training iterations.")
+parser.add_argument("--export_io_descriptors", action="store_true", default=False, help="Export IO descriptors.")
 parser.add_argument(
     "--keep_all_info",
     action="store_true",
@@ -77,6 +78,7 @@ import os
 import random
 from datetime import datetime
 
+import omni
 from stable_baselines3 import PPO
 from stable_baselines3.common.callbacks import CheckpointCallback, LogEveryNTimesteps
 from stable_baselines3.common.vec_env import VecNormalize
@@ -141,6 +143,15 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
     policy_arch = agent_cfg.pop("policy")
     n_timesteps = agent_cfg.pop("n_timesteps")
 
+    # set the IO descriptors output directory if requested
+    if isinstance(env_cfg, ManagerBasedRLEnvCfg):
+        env_cfg.export_io_descriptors = args_cli.export_io_descriptors
+        env_cfg.io_descriptors_output_dir = log_dir
+    else:
+        omni.log.warn(
+            "IO descriptors are only supported for manager based RL environments. No IO descriptors will be exported."
+        )
+
     # create isaac environment
     env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None)
 

scripts/reinforcement_learning/skrl/train.py

@@ -39,6 +39,7 @@ parser.add_argument(
 )
 parser.add_argument("--checkpoint", type=str, default=None, help="Path to model checkpoint to resume training.")
 parser.add_argument("--max_iterations", type=int, default=None, help="RL Policy training iterations.")
+parser.add_argument("--export_io_descriptors", action="store_true", default=False, help="Export IO descriptors.")
 parser.add_argument(
     "--ml_framework",
     type=str,
@@ -76,6 +77,7 @@ import os
 import random
 from datetime import datetime
 
+import omni
 import skrl
 from packaging import version
 
@@ -171,6 +173,15 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
     # get checkpoint path (to resume training)
     resume_path = retrieve_file_path(args_cli.checkpoint) if args_cli.checkpoint else None
 
+    # set the IO descriptors output directory if requested
+    if isinstance(env_cfg, ManagerBasedRLEnvCfg):
+        env_cfg.export_io_descriptors = args_cli.export_io_descriptors
+        env_cfg.io_descriptors_output_dir = os.path.join(log_root_path, log_dir)
+    else:
+        omni.log.warn(
+            "IO descriptors are only supported for manager based RL environments. No IO descriptors will be exported."
+        )
+
     # create isaac environment
     env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None)
 

source/isaaclab/docs/CHANGELOG.rst

 Changelog
 ---------
 
+0.45.2 (2025-08-18)
+~~~~~~~~~~~~~~~~~~~
+
+Added
+^^^^^
+
+* Added :meth:`~isaaclab.managers.ObservationManager.get_IO_descriptors` to export the IO descriptors for the observation manager.
+* Added :meth:`~isaaclab.envs.ManagerBasedEnvCfg.io_descriptors_output_dir` to configure the directory to export the IO descriptors to.
+* Added :meth:`~isaaclab.envs.ManagerBasedEnvCfg.export_io_descriptors` to toggle the export of the IO descriptors.
+* Added the option to export the Observation and Action of the managed environments into a YAML file. This can be used to more easily
+  deploy policies trained in Isaac Lab.
+
+
 0.45.1 (2025-08-16)
 ~~~~~~~~~~~~~~~~~~~
 

source/isaaclab/isaaclab/envs/manager_based_env.py

@@ -24,6 +24,7 @@ from isaaclab.utils.timer import Timer
 from .common import VecEnvObs
 from .manager_based_env_cfg import ManagerBasedEnvCfg
 from .ui import ViewportCameraController
+from .utils.io_descriptors import export_articulations_data, export_scene_data
 
 
 class ManagerBasedEnv:
@@ -185,6 +186,10 @@ class ManagerBasedEnv:
         # initialize observation buffers
         self.obs_buf = {}
 
+        # export IO descriptors if requested
+        if self.cfg.export_io_descriptors:
+            self.export_IO_descriptors()
+
     def __del__(self):
         """Cleanup for the environment."""
         self.close()
@@ -219,6 +224,46 @@ class ManagerBasedEnv:
         """The device on which the environment is running."""
         return self.sim.device
 
+    @property
+    def get_IO_descriptors(self):
+        """Get the IO descriptors for the environment.
+
+        Returns:
+            A dictionary with keys as the group names and values as the IO descriptors.
+        """
+        return {
+            "observations": self.observation_manager.get_IO_descriptors,
+            "actions": self.action_manager.get_IO_descriptors,
+            "articulations": export_articulations_data(self),
+            "scene": export_scene_data(self),
+        }
+
+    def export_IO_descriptors(self, output_dir: str | None = None):
+        """Export the IO descriptors for the environment.
+
+        Args:
+            output_dir: The directory to export the IO descriptors to.
+        """
+        import os
+        import yaml
+
+        IO_descriptors = self.get_IO_descriptors
+
+        if output_dir is None:
+            output_dir = self.cfg.io_descriptors_output_dir
+        if output_dir is None:
+            raise ValueError(
+                "Output directory is not set. Please set the output directory using the `io_descriptors_output_dir`"
+                " configuration."
+            )
+
+        if not os.path.exists(output_dir):
+            os.makedirs(output_dir, exist_ok=True)
+
+        with open(os.path.join(output_dir, "IO_descriptors.yaml"), "w") as f:
+            print(f"[INFO]: Exporting IO descriptors to {os.path.join(output_dir, 'IO_descriptors.yaml')}")
+            yaml.safe_dump(IO_descriptors, f)
+
     """
     Operations - Setup.
     """

source/isaaclab/isaaclab/envs/manager_based_env_cfg.py

@@ -125,3 +125,9 @@ class ManagerBasedEnvCfg:
 
     teleop_devices: DevicesCfg = field(default_factory=DevicesCfg)
     """Configuration for teleoperation devices."""
+
+    export_io_descriptors: bool = False
+    """Whether to export the IO descriptors for the environment. Defaults to False."""
+
+    io_descriptors_output_dir: str | None = None
+    """The directory to export the IO descriptors to. Defaults to None."""

source/isaaclab/isaaclab/envs/mdp/actions/binary_joint_actions.py

@@ -17,6 +17,7 @@ from isaaclab.managers.action_manager import ActionTerm
 
 if TYPE_CHECKING:
     from isaaclab.envs import ManagerBasedEnv
+    from isaaclab.envs.utils.io_descriptors import GenericActionIODescriptor
 
     from . import actions_cfg
 
@@ -111,6 +112,15 @@ class BinaryJointAction(ActionTerm):
     def processed_actions(self) -> torch.Tensor:
         return self._processed_actions
 
+    @property
+    def IO_descriptor(self) -> GenericActionIODescriptor:
+        super().IO_descriptor
+        self._IO_descriptor.shape = (self.action_dim,)
+        self._IO_descriptor.dtype = str(self.raw_actions.dtype)
+        self._IO_descriptor.action_type = "JointAction"
+        self._IO_descriptor.joint_names = self._joint_names
+        return self._IO_descriptor
+
     """
     Operations.
     """

source/isaaclab/isaaclab/envs/mdp/actions/joint_actions.py

@@ -17,6 +17,7 @@ from isaaclab.managers.action_manager import ActionTerm
 
 if TYPE_CHECKING:
     from isaaclab.envs import ManagerBasedEnv
+    from isaaclab.envs.utils.io_descriptors import GenericActionIODescriptor
 
     from . import actions_cfg
 
@@ -123,6 +124,41 @@ class JointAction(ActionTerm):
     def processed_actions(self) -> torch.Tensor:
         return self._processed_actions
 
+    @property
+    def IO_descriptor(self) -> GenericActionIODescriptor:
+        """The IO descriptor of the action term.
+
+        This descriptor is used to describe the action term of the joint action.
+        It adds the following information to the base descriptor:
+        - joint_names: The names of the joints.
+        - scale: The scale of the action term.
+        - offset: The offset of the action term.
+        - clip: The clip of the action term.
+
+        Returns:
+            The IO descriptor of the action term.
+        """
+        super().IO_descriptor
+        self._IO_descriptor.shape = (self.action_dim,)
+        self._IO_descriptor.dtype = str(self.raw_actions.dtype)
+        self._IO_descriptor.action_type = "JointAction"
+        self._IO_descriptor.joint_names = self._joint_names
+        self._IO_descriptor.scale = self._scale
+        # This seems to be always [4xNum_joints] IDK why. Need to check.
+        if isinstance(self._offset, torch.Tensor):
+            self._IO_descriptor.offset = self._offset[0].detach().cpu().numpy().tolist()
+        else:
+            self._IO_descriptor.offset = self._offset
+        # FIXME: This is not correct. Add list support.
+        if self.cfg.clip is not None:
+            if isinstance(self._clip, torch.Tensor):
+                self._IO_descriptor.clip = self._clip[0].detach().cpu().numpy().tolist()
+            else:
+                self._IO_descriptor.clip = self._clip
+        else:
+            self._IO_descriptor.clip = None
+        return self._IO_descriptor
+
     """
     Operations.
     """

source/isaaclab/isaaclab/envs/mdp/actions/joint_actions_to_limits.py

@@ -18,6 +18,7 @@ from isaaclab.managers.action_manager import ActionTerm
 
 if TYPE_CHECKING:
     from isaaclab.envs import ManagerBasedEnv
+    from isaaclab.envs.utils.io_descriptors import GenericActionIODescriptor
 
     from . import actions_cfg
 
@@ -105,6 +106,37 @@ class JointPositionToLimitsAction(ActionTerm):
     def processed_actions(self) -> torch.Tensor:
         return self._processed_actions
 
+    @property
+    def IO_descriptor(self) -> GenericActionIODescriptor:
+        """The IO descriptor of the action term.
+
+        This descriptor is used to describe the action term of the joint position to limits action.
+        It adds the following information to the base descriptor:
+        - joint_names: The names of the joints.
+        - scale: The scale of the action term.
+        - offset: The offset of the action term.
+        - clip: The clip of the action term.
+
+        Returns:
+            The IO descriptor of the action term.
+        """
+        super().IO_descriptor
+        self._IO_descriptor.shape = (self.action_dim,)
+        self._IO_descriptor.dtype = str(self.raw_actions.dtype)
+        self._IO_descriptor.action_type = "JointAction"
+        self._IO_descriptor.joint_names = self._joint_names
+        self._IO_descriptor.scale = self._scale
+        # This seems to be always [4xNum_joints] IDK why. Need to check.
+        if isinstance(self._offset, torch.Tensor):
+            self._IO_descriptor.offset = self._offset[0].detach().cpu().numpy().tolist()
+        else:
+            self._IO_descriptor.offset = self._offset
+        if self.cfg.clip is not None:
+            self._IO_descriptor.clip = self._clip
+        else:
+            self._IO_descriptor.clip = None
+        return self._IO_descriptor
+
     """
     Operations.
     """
@@ -195,6 +227,33 @@ class EMAJointPositionToLimitsAction(JointPositionToLimitsAction):
         # initialize the previous targets
         self._prev_applied_actions = torch.zeros_like(self.processed_actions)
 
+    @property
+    def IO_descriptor(self) -> GenericActionIODescriptor:
+        """The IO descriptor of the action term.
+
+        This descriptor is used to describe the action term of the EMA joint position to limits action.
+        It adds the following information to the base descriptor:
+        - joint_names: The names of the joints.
+        - scale: The scale of the action term.
+        - offset: The offset of the action term.
+        - clip: The clip of the action term.
+        - alpha: The moving average weight.
+
+        Returns:
+            The IO descriptor of the action term.
+        """
+        super().IO_descriptor
+        if isinstance(self._alpha, float):
+            self._IO_descriptor.alpha = self._alpha
+        elif isinstance(self._alpha, torch.Tensor):
+            self._IO_descriptor.alpha = self._alpha[0].detach().cpu().numpy().tolist()
+        else:
+            raise ValueError(
+                f"Unsupported moving average weight type: {type(self._alpha)}. Supported types are float and"
+                " torch.Tensor."
+            )
+        return self._IO_descriptor
+
     def reset(self, env_ids: Sequence[int] | None = None) -> None:
         # check if specific environment ids are provided
         if env_ids is None:

source/isaaclab/isaaclab/envs/mdp/actions/non_holonomic_actions.py

@@ -18,6 +18,7 @@ from isaaclab.utils.math import euler_xyz_from_quat
 
 if TYPE_CHECKING:
     from isaaclab.envs import ManagerBasedEnv
+    from isaaclab.envs.utils.io_descriptors import GenericActionIODescriptor
 
     from . import actions_cfg
 
@@ -134,6 +135,36 @@ class NonHolonomicAction(ActionTerm):
     def processed_actions(self) -> torch.Tensor:
         return self._processed_actions
 
+    @property
+    def IO_descriptor(self) -> GenericActionIODescriptor:
+        """The IO descriptor of the action term.
+
+        This descriptor is used to describe the action term of the non-holonomic action.
+        It adds the following information to the base descriptor:
+        - scale: The scale of the action term.
+        - offset: The offset of the action term.
+        - clip: The clip of the action term.
+        - body_name: The name of the body.
+        - x_joint_name: The name of the x joint.
+        - y_joint_name: The name of the y joint.
+        - yaw_joint_name: The name of the yaw joint.
+
+        Returns:
+            The IO descriptor of the action term.
+        """
+        super().IO_descriptor
+        self._IO_descriptor.shape = (self.action_dim,)
+        self._IO_descriptor.dtype = str(self.raw_actions.dtype)
+        self._IO_descriptor.action_type = "non holonomic actions"
+        self._IO_descriptor.scale = self._scale
+        self._IO_descriptor.offset = self._offset
+        self._IO_descriptor.clip = self._clip
+        self._IO_descriptor.body_name = self._body_name
+        self._IO_descriptor.x_joint_name = self._joint_names[0]
+        self._IO_descriptor.y_joint_name = self._joint_names[1]
+        self._IO_descriptor.yaw_joint_name = self._joint_names[2]
+        return self._IO_descriptor
+
     """
     Operations.
     """

source/isaaclab/isaaclab/envs/mdp/actions/pink_task_space_actions.py

@@ -17,6 +17,7 @@ from isaaclab.managers.action_manager import ActionTerm
 
 if TYPE_CHECKING:
     from isaaclab.envs import ManagerBasedEnv
+    from isaaclab.envs.utils.io_descriptors import GenericActionIODescriptor
 
     from . import pink_actions_cfg
 
@@ -125,6 +126,31 @@ class PinkInverseKinematicsAction(ActionTerm):
         """Get the processed actions tensor."""
         return self._processed_actions
 
+    @property
+    def IO_descriptor(self) -> GenericActionIODescriptor:
+        """The IO descriptor of the action term.
+
+        This descriptor is used to describe the action term of the pink inverse kinematics action.
+        It adds the following information to the base descriptor:
+        - scale: The scale of the action term.
+        - offset: The offset of the action term.
+        - clip: The clip of the action term.
+        - pink_controller_joint_names: The names of the pink controller joints.
+        - hand_joint_names: The names of the hand joints.
+        - controller_cfg: The configuration of the pink controller.
+
+        Returns:
+            The IO descriptor of the action term.
+        """
+        super().IO_descriptor
+        self._IO_descriptor.shape = (self.action_dim,)
+        self._IO_descriptor.dtype = str(self.raw_actions.dtype)
+        self._IO_descriptor.action_type = "PinkInverseKinematicsAction"
+        self._IO_descriptor.pink_controller_joint_names = self._pink_controlled_joint_names
+        self._IO_descriptor.hand_joint_names = self._hand_joint_names
+        self._IO_descriptor.extras["controller_cfg"] = self.cfg.controller.__dict__
+        return self._IO_descriptor
+
     # """
     # Operations.
     # """

source/isaaclab/isaaclab/envs/mdp/actions/task_space_actions.py

@@ -23,6 +23,7 @@ from isaaclab.sim.utils import find_matching_prims
 
 if TYPE_CHECKING:
     from isaaclab.envs import ManagerBasedEnv
+    from isaaclab.envs.utils.io_descriptors import GenericActionIODescriptor
 
     from . import actions_cfg
 
@@ -148,6 +149,37 @@ class DifferentialInverseKinematicsAction(ActionTerm):
         jacobian[:, 3:, :] = torch.bmm(base_rot_matrix, jacobian[:, 3:, :])
         return jacobian
 
+    @property
+    def IO_descriptor(self) -> GenericActionIODescriptor:
+        """The IO descriptor of the action term.
+
+        This descriptor is used to describe the action term of the pink inverse kinematics action.
+        It adds the following information to the base descriptor:
+        - body_name: The name of the body.
+        - joint_names: The names of the joints.
+        - scale: The scale of the action term.
+        - clip: The clip of the action term.
+        - controller_cfg: The configuration of the controller.
+        - body_offset: The offset of the body.
+
+        Returns:
+            The IO descriptor of the action term.
+        """
+        super().IO_descriptor
+        self._IO_descriptor.shape = (self.action_dim,)
+        self._IO_descriptor.dtype = str(self.raw_actions.dtype)
+        self._IO_descriptor.action_type = "TaskSpaceAction"
+        self._IO_descriptor.body_name = self._body_name
+        self._IO_descriptor.joint_names = self._joint_names
+        self._IO_descriptor.scale = self._scale
+        if self.cfg.clip is not None:
+            self._IO_descriptor.clip = self.cfg.clip
+        else:
+            self._IO_descriptor.clip = None
+        self._IO_descriptor.extras["controller_cfg"] = self.cfg.controller.__dict__
+        self._IO_descriptor.extras["body_offset"] = self.cfg.body_offset.__dict__
+        return self._IO_descriptor
+
     """
     Operations.
     """
@@ -409,6 +441,47 @@ class OperationalSpaceControllerAction(ActionTerm):
         jacobian[:, 3:, :] = torch.bmm(base_rot_matrix, jacobian[:, 3:, :])
         return jacobian
 
+    @property
+    def IO_descriptor(self) -> GenericActionIODescriptor:
+        """The IO descriptor of the action term.
+
+        This descriptor is used to describe the action term of the pink inverse kinematics action.
+        It adds the following information to the base descriptor:
+        - body_name: The name of the body.
+        - joint_names: The names of the joints.
+        - position_scale: The scale of the position.
+        - orientation_scale: The scale of the orientation.
+        - wrench_scale: The scale of the wrench.
+        - stiffness_scale: The scale of the stiffness.
+        - damping_ratio_scale: The scale of the damping ratio.
+        - nullspace_joint_pos_target: The nullspace joint pos target.
+        - clip: The clip of the action term.
+        - controller_cfg: The configuration of the controller.
+        - body_offset: The offset of the body.
+
+        Returns:
+            The IO descriptor of the action term.
+        """
+        super().IO_descriptor
+        self._IO_descriptor.shape = (self.action_dim,)
+        self._IO_descriptor.dtype = str(self.raw_actions.dtype)
+        self._IO_descriptor.action_type = "TaskSpaceAction"
+        self._IO_descriptor.body_name = self._ee_body_name
+        self._IO_descriptor.joint_names = self._joint_names
+        self._IO_descriptor.position_scale = self.cfg.position_scale
+        self._IO_descriptor.orientation_scale = self.cfg.orientation_scale
+        self._IO_descriptor.wrench_scale = self.cfg.wrench_scale
+        self._IO_descriptor.stiffness_scale = self.cfg.stiffness_scale
+        self._IO_descriptor.damping_ratio_scale = self.cfg.damping_ratio_scale
+        self._IO_descriptor.nullspace_joint_pos_target = self.cfg.nullspace_joint_pos_target
+        if self.cfg.clip is not None:
+            self._IO_descriptor.clip = self.cfg.clip
+        else:
+            self._IO_descriptor.clip = None
+        self._IO_descriptor.extras["controller_cfg"] = self.cfg.controller_cfg.__dict__
+        self._IO_descriptor.extras["body_offset"] = self.cfg.body_offset.__dict__
+        return self._IO_descriptor
+
     """
     Operations.
     """

source/isaaclab/isaaclab/envs/mdp/observations.py

@@ -24,12 +24,22 @@ from isaaclab.sensors import Camera, Imu, RayCaster, RayCasterCamera, TiledCamer
 if TYPE_CHECKING:
     from isaaclab.envs import ManagerBasedEnv, ManagerBasedRLEnv
 
+from isaaclab.envs.utils.io_descriptors import (
+    generic_io_descriptor,
+    record_body_names,
+    record_dtype,
+    record_joint_names,
+    record_joint_pos_offsets,
+    record_joint_vel_offsets,
+    record_shape,
+)
 
 """
 Root state.
 """
 
 
+@generic_io_descriptor(units="m", axes=["Z"], observation_type="RootState", on_inspect=[record_shape, record_dtype])
 def base_pos_z(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
     """Root height in the simulation world frame."""
     # extract the used quantities (to enable type-hinting)
@@ -37,6 +47,9 @@ def base_pos_z(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg(
     return asset.data.root_pos_w[:, 2].unsqueeze(-1)
 
 
+@generic_io_descriptor(
+    units="m/s", axes=["X", "Y", "Z"], observation_type="RootState", on_inspect=[record_shape, record_dtype]
+)
 def base_lin_vel(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
     """Root linear velocity in the asset's root frame."""
     # extract the used quantities (to enable type-hinting)
@@ -44,6 +57,9 @@ def base_lin_vel(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCf
     return asset.data.root_lin_vel_b
 
 
+@generic_io_descriptor(
+    units="rad/s", axes=["X", "Y", "Z"], observation_type="RootState", on_inspect=[record_shape, record_dtype]
+)
 def base_ang_vel(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
     """Root angular velocity in the asset's root frame."""
     # extract the used quantities (to enable type-hinting)
@@ -51,6 +67,9 @@ def base_ang_vel(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCf
     return asset.data.root_ang_vel_b
 
 
+@generic_io_descriptor(
+    units="m/s^2", axes=["X", "Y", "Z"], observation_type="RootState", on_inspect=[record_shape, record_dtype]
+)
 def projected_gravity(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
     """Gravity projection on the asset's root frame."""
     # extract the used quantities (to enable type-hinting)
@@ -58,6 +77,9 @@ def projected_gravity(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEnt
     return asset.data.projected_gravity_b
 
 
+@generic_io_descriptor(
+    units="m", axes=["X", "Y", "Z"], observation_type="RootState", on_inspect=[record_shape, record_dtype]
+)
 def root_pos_w(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
     """Asset root position in the environment frame."""
     # extract the used quantities (to enable type-hinting)
@@ -65,6 +87,9 @@ def root_pos_w(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg(
     return asset.data.root_pos_w - env.scene.env_origins
 
 
+@generic_io_descriptor(
+    units="unit", axes=["W", "X", "Y", "Z"], observation_type="RootState", on_inspect=[record_shape, record_dtype]
+)
 def root_quat_w(
     env: ManagerBasedEnv, make_quat_unique: bool = False, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")
 ) -> torch.Tensor:
@@ -82,6 +107,9 @@ def root_quat_w(
     return math_utils.quat_unique(quat) if make_quat_unique else quat
 
 
+@generic_io_descriptor(
+    units="m/s", axes=["X", "Y", "Z"], observation_type="RootState", on_inspect=[record_shape, record_dtype]
+)
 def root_lin_vel_w(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
     """Asset root linear velocity in the environment frame."""
     # extract the used quantities (to enable type-hinting)
@@ -89,6 +117,9 @@ def root_lin_vel_w(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntity
     return asset.data.root_lin_vel_w
 
 
+@generic_io_descriptor(
+    units="rad/s", axes=["X", "Y", "Z"], observation_type="RootState", on_inspect=[record_shape, record_dtype]
+)
 def root_ang_vel_w(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
     """Asset root angular velocity in the environment frame."""
     # extract the used quantities (to enable type-hinting)
@@ -101,6 +132,7 @@ Body state
 """
 
 
+@generic_io_descriptor(observation_type="BodyState", on_inspect=[record_shape, record_dtype, record_body_names])
 def body_pose_w(
     env: ManagerBasedEnv,
     asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
@@ -126,6 +158,7 @@ def body_pose_w(
     return pose.reshape(env.num_envs, -1)
 
 
+@generic_io_descriptor(observation_type="BodyState", on_inspect=[record_shape, record_dtype, record_body_names])
 def body_projected_gravity_b(
     env: ManagerBasedEnv,
     asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
@@ -155,6 +188,9 @@ Joint state.
 """
 
 
+@generic_io_descriptor(
+    observation_type="JointState", on_inspect=[record_joint_names, record_dtype, record_shape], units="rad"
+)
 def joint_pos(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
     """The joint positions of the asset.
 
@@ -165,6 +201,11 @@ def joint_pos(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("
     return asset.data.joint_pos[:, asset_cfg.joint_ids]
 
 
+@generic_io_descriptor(
+    observation_type="JointState",
+    on_inspect=[record_joint_names, record_dtype, record_shape, record_joint_pos_offsets],
+    units="rad",
+)
 def joint_pos_rel(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
     """The joint positions of the asset w.r.t. the default joint positions.
 
@@ -175,6 +216,7 @@ def joint_pos_rel(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityC
     return asset.data.joint_pos[:, asset_cfg.joint_ids] - asset.data.default_joint_pos[:, asset_cfg.joint_ids]
 
 
+@generic_io_descriptor(observation_type="JointState", on_inspect=[record_joint_names, record_dtype, record_shape])
 def joint_pos_limit_normalized(
     env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")
 ) -> torch.Tensor:
@@ -191,6 +233,9 @@ def joint_pos_limit_normalized(
     )
 
 
+@generic_io_descriptor(
+    observation_type="JointState", on_inspect=[record_joint_names, record_dtype, record_shape], units="rad/s"
+)
 def joint_vel(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")):
     """The joint velocities of the asset.
 
@@ -201,6 +246,11 @@ def joint_vel(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("
     return asset.data.joint_vel[:, asset_cfg.joint_ids]
 
 
+@generic_io_descriptor(
+    observation_type="JointState",
+    on_inspect=[record_joint_names, record_dtype, record_shape, record_joint_vel_offsets],
+    units="rad/s",
+)
 def joint_vel_rel(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")):
     """The joint velocities of the asset w.r.t. the default joint velocities.
 
@@ -211,6 +261,9 @@ def joint_vel_rel(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityC
     return asset.data.joint_vel[:, asset_cfg.joint_ids] - asset.data.default_joint_vel[:, asset_cfg.joint_ids]
 
 
+@generic_io_descriptor(
+    observation_type="JointState", on_inspect=[record_joint_names, record_dtype, record_shape], units="N.m"
+)
 def joint_effort(env: ManagerBasedEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
     """The joint applied effort of the robot.
 
@@ -597,6 +650,7 @@ Actions.
 """
 
 
+@generic_io_descriptor(dtype=torch.float32, observation_type="Action", on_inspect=[record_shape])
 def last_action(env: ManagerBasedEnv, action_name: str | None = None) -> torch.Tensor:
     """The last input action to the environment.
 
@@ -614,7 +668,8 @@ Commands.
 """
 
 
-def generated_commands(env: ManagerBasedRLEnv, command_name: str) -> torch.Tensor:
+@generic_io_descriptor(dtype=torch.float32, observation_type="Command", on_inspect=[record_shape])
+def generated_commands(env: ManagerBasedRLEnv, command_name: str | None = None) -> torch.Tensor:
     """The generated command from command term in the command manager with the given name."""
     return env.command_manager.get_command(command_name)
 

source/isaaclab/isaaclab/managers/action_manager.py

@@ -8,16 +8,18 @@
 from __future__ import annotations
 
 import inspect
+import re
 import torch
 import weakref
 from abc import abstractmethod
 from collections.abc import Sequence
 from prettytable import PrettyTable
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
 
 import omni.kit.app
 
 from isaaclab.assets import AssetBase
+from isaaclab.envs.utils.io_descriptors import GenericActionIODescriptor
 
 from .manager_base import ManagerBase, ManagerTermBase
 from .manager_term_cfg import ActionTermCfg
@@ -50,6 +52,8 @@ class ActionTerm(ManagerTermBase):
         super().__init__(cfg, env)
         # parse config to obtain asset to which the term is applied
         self._asset: AssetBase = self._env.scene[self.cfg.asset_name]
+        self._IO_descriptor = GenericActionIODescriptor()
+        self._export_IO_descriptor = True
 
         # add handle for debug visualization (this is set to a valid handle inside set_debug_vis)
         self._debug_vis_handle = None
@@ -91,6 +95,20 @@ class ActionTerm(ManagerTermBase):
         source_code = inspect.getsource(self._set_debug_vis_impl)
         return "NotImplementedError" not in source_code
 
+    @property
+    def IO_descriptor(self) -> GenericActionIODescriptor:
+        """The IO descriptor for the action term."""
+        self._IO_descriptor.name = re.sub(r"([a-z])([A-Z])", r"\1_\2", self.__class__.__name__).lower()
+        self._IO_descriptor.full_path = f"{self.__class__.__module__}.{self.__class__.__name__}"
+        self._IO_descriptor.description = " ".join(self.__class__.__doc__.split())
+        self._IO_descriptor.export = self.export_IO_descriptor
+        return self._IO_descriptor
+
+    @property
+    def export_IO_descriptor(self) -> bool:
+        """Whether to export the IO descriptor for the action term."""
+        return self._export_IO_descriptor
+
     """
     Operations.
     """
@@ -259,6 +277,41 @@ class ActionManager(ManagerBase):
             has_debug_vis |= term.has_debug_vis_implementation
         return has_debug_vis
 
+    @property
+    def get_IO_descriptors(self) -> list[dict[str, Any]]:
+        """Get the IO descriptors for the action manager.
+
+        Returns:
+            A dictionary with keys as the term names and values as the IO descriptors.
+        """
+
+        data = []
+
+        for term_name, term in self._terms.items():
+            try:
+                data.append(term.IO_descriptor.__dict__.copy())
+            except Exception as e:
+                print(f"Error getting IO descriptor for term '{term_name}': {e}")
+
+        formatted_data = []
+        for item in data:
+            name = item.pop("name")
+            formatted_item = {"name": name, "extras": item.pop("extras")}
+            print(item["export"])
+            if not item.pop("export"):
+                continue
+            for k, v in item.items():
+                # Check if v is a tuple and convert to list
+                if isinstance(v, tuple):
+                    v = list(v)
+                if k in ["description", "units"]:
+                    formatted_item["extras"][k] = v
+                else:
+                    formatted_item[k] = v
+            formatted_data.append(formatted_item)
+
+        return formatted_data
+
     """
     Operations.
     """

source/isaaclab/isaaclab/managers/observation_manager.py

@@ -225,6 +225,70 @@ class ObservationManager(ManagerBase):
         """
         return self._group_obs_concatenate
 
+    @property
+    def get_IO_descriptors(self, group_names_to_export: list[str] = ["policy"]):
+        """Get the IO descriptors for the observation manager.
+
+        Returns:
+            A dictionary with keys as the group names and values as the IO descriptors.
+        """
+
+        group_data = {}
+
+        for group_name in self._group_obs_term_names:
+            group_data[group_name] = []
+            # check if group name is valid
+            if group_name not in self._group_obs_term_names:
+                raise ValueError(
+                    f"Unable to find the group '{group_name}' in the observation manager."
+                    f" Available groups are: {list(self._group_obs_term_names.keys())}"
+                )
+            # iterate over all the terms in each group
+            group_term_names = self._group_obs_term_names[group_name]
+            # read attributes for each term
+            obs_terms = zip(group_term_names, self._group_obs_term_cfgs[group_name])
+
+            for term_name, term_cfg in obs_terms:
+                # Call to the observation function to get the IO descriptor with the inspect flag set to True
+                try:
+                    term_cfg.func(self._env, **term_cfg.params, inspect=True)
+                    # Copy the descriptor and update with the term's own extra parameters
+                    desc = term_cfg.func._descriptor.__dict__.copy()
+                    # Create a dictionary to store the overloads
+                    overloads = {}
+                    # Iterate over the term's own parameters and add them to the overloads dictionary
+                    for k, v in term_cfg.__dict__.items():
+                        # For now we do not add the noise modifier
+                        if k in ["modifiers", "clip", "scale", "history_length", "flatten_history_dim"]:
+                            overloads[k] = v
+                    desc.update(overloads)
+                    group_data[group_name].append(desc)
+                except Exception as e:
+                    print(f"Error getting IO descriptor for term '{term_name}' in group '{group_name}': {e}")
+        # Format the data for YAML export
+        formatted_data = {}
+        for group_name, data in group_data.items():
+            formatted_data[group_name] = []
+            for item in data:
+                name = item.pop("name")
+                formatted_item = {"name": name, "overloads": {}, "extras": item.pop("extras")}
+                for k, v in item.items():
+                    # Check if v is a tuple and convert to list
+                    if isinstance(v, tuple):
+                        v = list(v)
+                    # Check if v is a tensor and convert to list
+                    if isinstance(v, torch.Tensor):
+                        v = v.detach().cpu().numpy().tolist()
+                    if k in ["scale", "clip", "history_length", "flatten_history_dim"]:
+                        formatted_item["overloads"][k] = v
+                    elif k in ["modifiers", "description", "units"]:
+                        formatted_item["extras"][k] = v
+                    else:
+                        formatted_item[k] = v
+                formatted_data[group_name].append(formatted_item)
+        formatted_data = {k: v for k, v in formatted_data.items() if k in group_names_to_export}
+        return formatted_data
+
     """
     Operations.
     """