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/_static/policy_deployment/01_io_descriptors/isaac_velocity_flat_g1_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:
+  - left_hip_pitch_joint
+  - right_hip_pitch_joint
+  - torso_joint
+  - left_hip_roll_joint
+  - right_hip_roll_joint
+  - left_shoulder_pitch_joint
+  - right_shoulder_pitch_joint
+  - left_hip_yaw_joint
+  - right_hip_yaw_joint
+  - left_shoulder_roll_joint
+  - right_shoulder_roll_joint
+  - left_knee_joint
+  - right_knee_joint
+  - left_shoulder_yaw_joint
+  - right_shoulder_yaw_joint
+  - left_ankle_pitch_joint
+  - right_ankle_pitch_joint
+  - left_elbow_pitch_joint
+  - right_elbow_pitch_joint
+  - left_ankle_roll_joint
+  - right_ankle_roll_joint
+  - left_elbow_roll_joint
+  - right_elbow_roll_joint
+  - left_five_joint
+  - left_three_joint
+  - left_zero_joint
+  - right_five_joint
+  - right_three_joint
+  - right_zero_joint
+  - left_six_joint
+  - left_four_joint
+  - left_one_joint
+  - right_six_joint
+  - right_four_joint
+  - right_one_joint
+  - left_two_joint
+  - right_two_joint
+  mdp_type: Action
+  name: joint_position_action
+  offset:
+  - -0.20000000298023224
+  - -0.20000000298023224
+  - 0.0
+  - 0.0
+  - 0.0
+  - 0.3499999940395355
+  - 0.3499999940395355
+  - 0.0
+  - 0.0
+  - 0.1599999964237213
+  - -0.1599999964237213
+  - 0.41999998688697815
+  - 0.41999998688697815
+  - 0.0
+  - 0.0
+  - -0.23000000417232513
+  - -0.23000000417232513
+  - 0.8700000047683716
+  - 0.8700000047683716
+  - 0.0
+  - 0.0
+  - 0.0
+  - 0.0
+  - 0.0
+  - 0.0
+  - 0.0
+  - 0.0
+  - 0.0
+  - 0.0
+  - 0.0
+  - 0.0
+  - 1.0
+  - 0.0
+  - 0.0
+  - -1.0
+  - 0.5199999809265137
+  - -0.5199999809265137
+  scale: 0.5
+  shape:
+  - 37
+articulations:
+  robot:
+    default_joint_armature:
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.009999999776482582
+    - 0.0010000000474974513
+    - 0.0010000000474974513
+    - 0.0010000000474974513
+    - 0.0010000000474974513
+    - 0.0010000000474974513
+    - 0.0010000000474974513
+    - 0.0010000000474974513
+    - 0.0010000000474974513
+    - 0.0010000000474974513
+    - 0.0010000000474974513
+    - 0.0010000000474974513
+    - 0.0010000000474974513
+    - 0.0010000000474974513
+    - 0.0010000000474974513
+    default_joint_damping:
+    - 5.0
+    - 5.0
+    - 5.0
+    - 5.0
+    - 5.0
+    - 10.0
+    - 10.0
+    - 5.0
+    - 5.0
+    - 10.0
+    - 10.0
+    - 5.0
+    - 5.0
+    - 10.0
+    - 10.0
+    - 2.0
+    - 2.0
+    - 10.0
+    - 10.0
+    - 2.0
+    - 2.0
+    - 10.0
+    - 10.0
+    - 10.0
+    - 10.0
+    - 10.0
+    - 10.0
+    - 10.0
+    - 10.0
+    - 10.0
+    - 10.0
+    - 10.0
+    - 10.0
+    - 10.0
+    - 10.0
+    - 10.0
+    - 10.0
+    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
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 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:
+    - -0.20000000298023224
+    - -0.20000000298023224
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.3499999940395355
+    - 0.3499999940395355
+    - 0.0
+    - 0.0
+    - 0.1599999964237213
+    - -0.1599999964237213
+    - 0.41999998688697815
+    - 0.41999998688697815
+    - 0.0
+    - 0.0
+    - -0.23000000417232513
+    - -0.23000000417232513
+    - 0.8700000047683716
+    - 0.8700000047683716
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 1.0
+    - 0.0
+    - 0.0
+    - -1.0
+    - 0.5199999809265137
+    - -0.5199999809265137
+    default_joint_pos_limits:
+    - - -2.3499996662139893
+      - 3.049999952316284
+    - - -2.3499996662139893
+      - 3.049999952316284
+    - - -2.618000030517578
+      - 2.618000030517578
+    - - -0.25999996066093445
+      - 2.5299997329711914
+    - - -2.5299997329711914
+      - 0.25999996066093445
+    - - -2.967099666595459
+      - 2.7924997806549072
+    - - -2.967099666595459
+      - 2.7924997806549072
+    - - -2.749999761581421
+      - 2.749999761581421
+    - - -2.749999761581421
+      - 2.749999761581421
+    - - -1.5881999731063843
+      - 2.251499652862549
+    - - -2.251499652862549
+      - 1.5881999731063843
+    - - -0.3348899781703949
+      - 2.5448997020721436
+    - - -0.3348899781703949
+      - 2.5448997020721436
+    - - -2.618000030517578
+      - 2.618000030517578
+    - - -2.618000030517578
+      - 2.618000030517578
+    - - -0.6799999475479126
+      - 0.7299999594688416
+    - - -0.6799999475479126
+      - 0.7299999594688416
+    - - -0.22679997980594635
+      - 3.420799732208252
+    - - -0.22679997980594635
+      - 3.420799732208252
+    - - -0.26179996132850647
+      - 0.26179996132850647
+    - - -0.26179996132850647
+      - 0.26179996132850647
+    - - -2.094299793243408
+      - 2.094299793243408
+    - - -2.094299793243408
+      - 2.094299793243408
+    - - -1.8399999141693115
+      - 0.30000001192092896
+    - - -1.8399999141693115
+      - 0.30000001192092896
+    - - -0.5235979557037354
+      - 0.5235979557037354
+    - - -0.30000001192092896
+      - 1.8399999141693115
+    - - -0.30000001192092896
+      - 1.8399999141693115
+    - - -0.5235979557037354
+      - 0.5235979557037354
+    - - -1.8399999141693115
+      - 0.0
+    - - -1.8399999141693115
+      - 0.0
+    - - -0.9999999403953552
+      - 1.2000000476837158
+    - - 0.0
+      - 1.8399999141693115
+    - - 0.0
+      - 1.8399999141693115
+    - - -1.2000000476837158
+      - 0.9999999403953552
+    - - 0.0
+      - 1.8399999141693115
+    - - -1.8399999141693115
+      - 0.0
+    default_joint_stiffness:
+    - 200.0
+    - 200.0
+    - 200.0
+    - 150.0
+    - 150.0
+    - 40.0
+    - 40.0
+    - 150.0
+    - 150.0
+    - 40.0
+    - 40.0
+    - 200.0
+    - 200.0
+    - 40.0
+    - 40.0
+    - 20.0
+    - 20.0
+    - 40.0
+    - 40.0
+    - 20.0
+    - 20.0
+    - 40.0
+    - 40.0
+    - 40.0
+    - 40.0
+    - 40.0
+    - 40.0
+    - 40.0
+    - 40.0
+    - 40.0
+    - 40.0
+    - 40.0
+    - 40.0
+    - 40.0
+    - 40.0
+    - 40.0
+    - 40.0
+    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
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 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:
+    - left_hip_pitch_joint
+    - right_hip_pitch_joint
+    - torso_joint
+    - left_hip_roll_joint
+    - right_hip_roll_joint
+    - left_shoulder_pitch_joint
+    - right_shoulder_pitch_joint
+    - left_hip_yaw_joint
+    - right_hip_yaw_joint
+    - left_shoulder_roll_joint
+    - right_shoulder_roll_joint
+    - left_knee_joint
+    - right_knee_joint
+    - left_shoulder_yaw_joint
+    - right_shoulder_yaw_joint
+    - left_ankle_pitch_joint
+    - right_ankle_pitch_joint
+    - left_elbow_pitch_joint
+    - right_elbow_pitch_joint
+    - left_ankle_roll_joint
+    - right_ankle_roll_joint
+    - left_elbow_roll_joint
+    - right_elbow_roll_joint
+    - left_five_joint
+    - left_three_joint
+    - left_zero_joint
+    - right_five_joint
+    - right_three_joint
+    - right_zero_joint
+    - left_six_joint
+    - left_four_joint
+    - left_one_joint
+    - right_six_joint
+    - right_four_joint
+    - right_one_joint
+    - left_two_joint
+    - right_two_joint
+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:
+    - left_hip_pitch_joint
+    - right_hip_pitch_joint
+    - torso_joint
+    - left_hip_roll_joint
+    - right_hip_roll_joint
+    - left_shoulder_pitch_joint
+    - right_shoulder_pitch_joint
+    - left_hip_yaw_joint
+    - right_hip_yaw_joint
+    - left_shoulder_roll_joint
+    - right_shoulder_roll_joint
+    - left_knee_joint
+    - right_knee_joint
+    - left_shoulder_yaw_joint
+    - right_shoulder_yaw_joint
+    - left_ankle_pitch_joint
+    - right_ankle_pitch_joint
+    - left_elbow_pitch_joint
+    - right_elbow_pitch_joint
+    - left_ankle_roll_joint
+    - right_ankle_roll_joint
+    - left_elbow_roll_joint
+    - right_elbow_roll_joint
+    - left_five_joint
+    - left_three_joint
+    - left_zero_joint
+    - right_five_joint
+    - right_three_joint
+    - right_zero_joint
+    - left_six_joint
+    - left_four_joint
+    - left_one_joint
+    - right_six_joint
+    - right_four_joint
+    - right_one_joint
+    - left_two_joint
+    - right_two_joint
+    joint_pos_offsets:
+    - -0.20000000298023224
+    - -0.20000000298023224
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.3499999940395355
+    - 0.3499999940395355
+    - 0.0
+    - 0.0
+    - 0.1599999964237213
+    - -0.1599999964237213
+    - 0.41999998688697815
+    - 0.41999998688697815
+    - 0.0
+    - 0.0
+    - -0.23000000417232513
+    - -0.23000000417232513
+    - 0.8700000047683716
+    - 0.8700000047683716
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 1.0
+    - 0.0
+    - 0.0
+    - -1.0
+    - 0.5199999809265137
+    - -0.5199999809265137
+    mdp_type: Observation
+    name: joint_pos_rel
+    observation_type: JointState
+    overloads:
+      clip: null
+      flatten_history_dim: true
+      history_length: 0
+      scale: null
+    shape:
+    - 37
+  - 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:
+    - left_hip_pitch_joint
+    - right_hip_pitch_joint
+    - torso_joint
+    - left_hip_roll_joint
+    - right_hip_roll_joint
+    - left_shoulder_pitch_joint
+    - right_shoulder_pitch_joint
+    - left_hip_yaw_joint
+    - right_hip_yaw_joint
+    - left_shoulder_roll_joint
+    - right_shoulder_roll_joint
+    - left_knee_joint
+    - right_knee_joint
+    - left_shoulder_yaw_joint
+    - right_shoulder_yaw_joint
+    - left_ankle_pitch_joint
+    - right_ankle_pitch_joint
+    - left_elbow_pitch_joint
+    - right_elbow_pitch_joint
+    - left_ankle_roll_joint
+    - right_ankle_roll_joint
+    - left_elbow_roll_joint
+    - right_elbow_roll_joint
+    - left_five_joint
+    - left_three_joint
+    - left_zero_joint
+    - right_five_joint
+    - right_three_joint
+    - right_zero_joint
+    - left_six_joint
+    - left_four_joint
+    - left_one_joint
+    - right_six_joint
+    - right_four_joint
+    - right_one_joint
+    - left_two_joint
+    - right_two_joint
+    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
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 0.0
+    - 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:
+    - 37
+  - 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:
+    - 37
+scene:
+  decimation: 4
+  dt: 0.02
+  physics_dt: 0.005

docs/source/policy_deployment/01_io_descriptors/io_descriptors_101.rst

+IO Descriptors 101
+==================
+
+.. currentmodule:: isaaclab
+
+In this tutorial, we will learn about IO descriptors, what they are, how to export them, and how to add them to
+your environments. We will use the Anymal-D robot as an example to demonstrate how to export IO descriptors from
+an environment, and use our own terms to demonstrate how to attach IO descriptors to custom action and observation terms.
+
+
+What are IO Descriptors?
+------------------------
+
+Before we dive into IO descriptors, let's first understand what they are and how they can be useful.
+
+IO descriptors are a way to describe the inputs and outputs of a policy trained using the ManagerBasedRLEnv in Isaac
+Lab. In other words, they describe the action and observation terms of a policy. This description is used to generate
+a YAML file that can be loaded in an external tool to run the policies without having to manually input the
+configuration of the action and observation terms.
+
+In addition to this the IO Descriptors provide the following information:
+- The parameters of all the joints in the articulation.
+- Some simulation parameters including the simulation time step, and the policy time step.
+- For some action and observation terms, it provides the joint names or body names in the same order as they appear in the action/observation terms.
+- For both the observation and action terms, it provides the terms in the exact same order as they appear in the managers. Making it easy to reconstruct them from the YAML file.
+
+Here is an example of what the action part of the YAML generated from the IO descriptors looks like for the Anymal-D robot:
+
+.. literalinclude:: ../../_static/policy_deployment/01_io_descriptors/isaac_velocity_flat_anymal_d_v0_IO_descriptors.yaml
+   :language: yaml
+   :lines: 1-39
+
+Here is an example of what a portion of the observation part of the YAML generated from the IO descriptors looks like for the Anymal-D robot:
+
+.. literalinclude:: ../../_static/policy_deployment/01_io_descriptors/isaac_velocity_flat_anymal_d_v0_IO_descriptors.yaml
+   :language: yaml
+   :lines: 158-199
+
+.. literalinclude:: ../../_static/policy_deployment/01_io_descriptors/isaac_velocity_flat_anymal_d_v0_IO_descriptors.yaml
+   :language: yaml
+   :lines: 236-279
+
+Something to note here is that both the action and observation terms are returned as list of dictionaries, and not a dictionary of dictionaries.
+This is done to ensure the order of the terms is preserved. Hence, to retrieve the action or observation term, the users need to look for the
+``name`` key in the dictionaries.
+
+For example, in the following snippet, we are looking at the ``projected_gravity`` observation term. The ``name`` key is used to identify the term.
+The ``full_path`` key is used to provide an explicit path to the function in Isaac Lab's source code that is used to compute this term. Some flags
+like ``mdp_type`` and ``observation_type`` are also provided, these don't have any functional impact. They are here to inform the user that this is the
+category this term belongs to.
+
+.. literalinclude:: ../../_static/policy_deployment/01_io_descriptors/isaac_velocity_flat_anymal_d_v0_IO_descriptors.yaml
+   :language: yaml
+   :lines: 200-219
+   :emphasize-lines: 9, 11
+
+
+Exporting IO Descriptors from an Environment
+--------------------------------------------
+
+In this section, we will cover how to export IO descriptors from an environment.
+Keep in mind that this feature is only available to the manager based RL environments.
+
+If a policy has already been trained using a given configuration, then the IO descriptors can be exported using:
+
+.. code-block:: bash
+
+   ./isaaclab.sh -p scripts/environments/export_io_descriptors.py --task <task_name> --output_dir <output_dir>
+
+For example, if we want to export the IO descriptors for the Anymal-D robot, we can run:
+
+.. code-block:: bash
+
+   ./isaaclab.sh -p scripts/environments/export_io_descriptors.py --task Isaac-Velocity-Flat-Anymal-D-v0 --output_dir ./io_descriptors
+
+When training a policy, it is also possible to request the IO descriptors to be exported at the beginning of the training.
+This can be done by setting the ``export_io_descriptors`` flag in the command line.
+
+.. code-block:: bash
+
+   ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Velocity-Flat-Anymal-D-v0 --export_io_descriptors
+   ./isaaclab.sh -p scripts/reinforcement_learning/sb3/train.py --task Isaac-Velocity-Flat-Anymal-D-v0 --export_io_descriptors
+   ./isaaclab.sh -p scripts/reinforcement_learning/rl_games/train.py --task Isaac-Velocity-Flat-Anymal-D-v0 --export_io_descriptors
+   ./isaaclab.sh -p scripts/reinforcement_learning/skrl/train.py --task Isaac-Velocity-Flat-Anymal-D-v0 --export_io_descriptors
+
+
+Attaching IO Descriptors to Custom Observation Terms
+----------------------------------------------------
+
+In this section, we will cover how to attach IO descriptors to custom observation terms.
+
+Let's take a look at how we can attach an IO descriptor to a simple observation term:
+
+.. code-block:: python
+
+   @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)
+       asset: RigidObject = env.scene[asset_cfg.name]
+       return asset.data.root_lin_vel_b
+
+Here, we are defining a custom observation term called ``base_lin_vel`` that computes the root linear velocity of the robot.
+We are also attaching an IO descriptor to this term. The IO descriptor is defined using the ``@generic_io_descriptor`` decorator.
+
+The ``@generic_io_descriptor`` decorator is a special decorator that is used to attach an IO descriptor to a custom observation term.
+It takes arbitrary arguments that are used to describe the observation term, in this case we provide extra information that could be
+useful for the end user:
+
+- ``units``: The units of the observation term.
+- ``axes``: The axes of the observation term.
+- ``observation_type``: The type of the observation term.
+
+You'll also notice that there is an ``on_inspect`` argument that is provided. This is a list of functions that are used to inspect the observation term.
+In this case, we are using the ``record_shape`` and ``record_dtype`` functions to record the shape and dtype of the output of the observation term.
+
+These functions are defined like so:
+
+.. code-block:: python
+
+   def record_shape(output: torch.Tensor, descriptor: GenericObservationIODescriptor, **kwargs) -> None:
+       """Record the shape of the output tensor.
+
+       Args:
+          output: The output tensor.
+          descriptor: The descriptor to record the shape to.
+          **kwargs: Additional keyword arguments.
+       """
+       descriptor.shape = (output.shape[-1],)
+
+
+   def record_dtype(output: torch.Tensor, descriptor: GenericObservationIODescriptor, **kwargs) -> None:
+       """Record the dtype of the output tensor.
+
+       Args:
+          output: The output tensor.
+          descriptor: The descriptor to record the dtype to.
+          **kwargs: Additional keyword arguments.
+       """
+       descriptor.dtype = str(output.dtype)
+
+They always take the output tensor of the observation term as the first argument, and the descriptor as the second argument.
+In the ``kwargs`` all the inputs of the observation term are provided. In addition to the ``on_inspect`` functions, the decorator
+will also call call some functions in the background to collect the ``name``, the ``description``, and the ``full_path`` of the
+observation term. Note that adding this decorator does not change the signature of the observation term, so it can be used safely
+with the observation manager!
+
+Let us now take a look at a more complex example: getting the relative joint positions of the robot.
+
+.. code-block:: python
+
+   @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.
+
+       Note: Only the joints configured in :attr:`asset_cfg.joint_ids` will have their positions returned.
+       """
+       # extract the used quantities (to enable type-hinting)
+       asset: Articulation = env.scene[asset_cfg.name]
+       return asset.data.joint_pos[:, asset_cfg.joint_ids] - asset.data.default_joint_pos[:, asset_cfg.joint_ids]
+
+Similarly to the previous example, we are adding an IO descriptor to a custom observation term with a set of functions that probe the observation term.
+
+To get the name of the joints we can write the following function:
+
+.. code-block:: python
+
+   def record_joint_names(output: torch.Tensor, descriptor: GenericObservationIODescriptor, **kwargs) -> None:
+       """Record the joint names of the output tensor.
+
+       Expects the `asset_cfg` keyword argument to be set.
+
+       Args:
+           output: The output tensor.
+           descriptor: The descriptor to record the joint names to.
+           **kwargs: Additional keyword arguments.
+       """
+       asset: Articulation = kwargs["env"].scene[kwargs["asset_cfg"].name]
+       joint_ids = kwargs["asset_cfg"].joint_ids
+       if joint_ids == slice(None, None, None):
+           joint_ids = list(range(len(asset.joint_names)))
+       descriptor.joint_names = [asset.joint_names[i] for i in joint_ids]
+
+Note that we can access all the inputs of the observation term in the ``kwargs`` dictionary. Hence we can access the ``asset_cfg``, which contains the
+configuration of the articulation that the observation term is computed on.
+
+To get the offsets, we can write the following function:
+
+.. code-block:: python
+
+   def record_joint_pos_offsets(output: torch.Tensor, descriptor: GenericObservationIODescriptor, **kwargs):
+    """Record the joint position offsets of the output tensor.
+
+    Expects the `asset_cfg` keyword argument to be set.
+
+    Args:
+        output: The output tensor.
+        descriptor: The descriptor to record the joint position offsets to.
+        **kwargs: Additional keyword arguments.
+    """
+       asset: Articulation = kwargs["env"].scene[kwargs["asset_cfg"].name]
+       ids = kwargs["asset_cfg"].joint_ids
+       # Get the offsets of the joints for the first robot in the scene.
+       # This assumes that all robots have the same joint offsets.
+       descriptor.joint_pos_offsets = asset.data.default_joint_pos[:, ids][0]
+
+With this in mind, you should now be able to attach an IO descriptor to your own custom observation terms! However, before
+we close this tutorial, let's take a look at how we can attach an IO descriptor to a custom action term.
+
+
+Attaching IO Descriptors to Custom Action Terms
+-----------------------------------------------
+
+In this section, we will cover how to attach IO descriptors to custom action terms. Action terms are classes that
+inherit from the :class:`managers.ActionTerm` class. To add an IO descriptor to an action term, we need to expand
+upon its :meth:`ActionTerm.IO_descriptor` property.
+
+By default, the :meth:`ActionTerm.IO_descriptor` property returns the base descriptor and fills the following fields:
+- ``name``: The name of the action term.
+- ``full_path``: The full path of the action term.
+- ``description``: The description of the action term.
+- ``export``: Whether to export the action term.
+
+.. code-block:: python
+
+   @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
+
+To add more information to the descriptor, we need to override the :meth:`ActionTerm.IO_descriptor` property.
+Let's take a look at an example on how to add the joint names, scale, offset, and clip to the descriptor.
+
+.. code-block:: python
+
+   @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
+
+This is it! You should now be able to attach an IO descriptor to your own custom action terms which concludes this tutorial.

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/envs/utils/io_descriptors.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
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from typing import TYPE_CHECKING, Any, Concatenate, ParamSpec, TypeVar
+
+from isaaclab.utils import configclass
+
+if TYPE_CHECKING:
+    from isaaclab.envs import ManagerBasedEnv
+    from isaaclab.assets.articulation import Articulation
+    import torch
+
+import dataclasses
+import functools
+import inspect
+
+
+@configclass
+class GenericActionIODescriptor:
+    """Generic action IO descriptor.
+
+    This descriptor is used to describe the action space of a policy.
+    It can be extended as needed to add more information about the action term that is being described.
+    """
+
+    mdp_type: str = "Action"
+    """The type of MDP that the action term belongs to."""
+
+    name: str = None
+    """The name of the action term.
+
+    By default, the name of the action term class is used.
+    """
+
+    full_path: str = None
+    """The full path of the action term class.
+
+    By default, python's will retrieve the path from the file that the action term class is defined in
+    and the name of the action term class.
+    """
+
+    description: str = None
+    """The description of the action term.
+
+    By default, the docstring of the action term class is used.
+    """
+
+    shape: tuple[int, ...] = None
+    """The shape of the action term.
+
+    This should be populated by the user."""
+
+    dtype: str = None
+    """The dtype of the action term.
+
+    This should be populated by the user."""
+
+    action_type: str = None
+    """The type of the action term.
+
+    This attribute is purely informative and should be populated by the user."""
+
+    extras: dict[str, Any] = {}
+    """Extra information about the action term.
+
+    This attribute is purely informative and should be populated by the user."""
+
+    export: bool = True
+    """Whether to export the action term.
+
+    Should be set to False if the class is not meant to be exported.
+    """
+
+
+@configclass
+class GenericObservationIODescriptor:
+    """Generic observation IO descriptor.
+
+    This descriptor is used to describe the observation space of a policy.
+    It can be extended as needed to add more information about the observation term that is being described.
+    """
+
+    mdp_type: str = "Observation"
+    name: str = None
+    full_path: str = None
+    description: str = None
+    shape: tuple[int, ...] = None
+    dtype: str = None
+    observation_type: str = None
+    extras: dict[str, Any] = {}
+
+
+# These are defined to help with type hinting
+P = ParamSpec("P")
+R = TypeVar("R")
+
+
+# Automatically builds a descriptor from the kwargs
+def _make_descriptor(**kwargs: Any) -> GenericObservationIODescriptor:
+    """Split *kwargs* into (known dataclass fields) and (extras)."""
+    field_names = {f.name for f in dataclasses.fields(GenericObservationIODescriptor)}
+    known = {k: v for k, v in kwargs.items() if k in field_names}
+    extras = {k: v for k, v in kwargs.items() if k not in field_names}
+
+    desc = GenericObservationIODescriptor(**known)
+    # User defined extras are stored in the descriptor under the `extras` field
+    desc.extras = extras
+    return desc
+
+
+# Decorator factory for generic IO descriptors.
+def generic_io_descriptor(
+    _func: Callable[Concatenate[ManagerBasedEnv, P], R] | None = None,
+    *,
+    on_inspect: Callable[..., Any] | list[Callable[..., Any]] | None = None,
+    **descriptor_kwargs: Any,
+) -> Callable[[Callable[Concatenate[ManagerBasedEnv, P], R]], Callable[Concatenate[ManagerBasedEnv, P], R]]:
+    """
+    Decorator factory for generic IO descriptors.
+
+    This decorator can be used in different ways:
+    1. The default decorator has all the information I need for my use case:
+    ..code-block:: python
+        @generic_io_descriptor(GenericIODescriptor(description="..", dtype=".."))
+        def my_func(env: ManagerBasedEnv, *args, **kwargs):
+            ...
+    ..note:: If description is not set, the function's docstring is used to populate it.
+
+    2. I need to add more information to the descriptor:
+    ..code-block:: python
+        @generic_io_descriptor(description="..", new_var_1="a", new_var_2="b")
+        def my_func(env: ManagerBasedEnv, *args, **kwargs):
+            ...
+    3. I need to add a hook to the descriptor:
+    ..code-block:: python
+        def record_shape(tensor: torch.Tensor, desc: GenericIODescriptor, **kwargs):
+            desc.shape = (tensor.shape[-1],)
+
+        @generic_io_descriptor(description="..", new_var_1="a", new_var_2="b", on_inspect=[record_shape, record_dtype])
+        def my_func(env: ManagerBasedEnv, *args, **kwargs):
+    ..note:: The hook is called after the function is called, if and only if the `inspect` flag is set when calling the function.
+
+    For example:
+    ..code-block:: python
+        my_func(env, inspect=True)
+
+    4. I need to add a hook to the descriptor and this hook will write to a variable that is not part of the base descriptor.
+    ..code-block:: python
+        def record_joint_names(output: torch.Tensor, descriptor: GenericIODescriptor, **kwargs):
+            asset: Articulation = kwargs["env"].scene[kwargs["asset_cfg"].name]
+            joint_ids = kwargs["asset_cfg"].joint_ids
+            if joint_ids == slice(None, None, None):
+                joint_ids = list(range(len(asset.joint_names)))
+            descriptor.joint_names = [asset.joint_names[i] for i in joint_ids]
+
+        @generic_io_descriptor(new_var_1="a", new_var_2="b", on_inspect=[record_shape, record_dtype, record_joint_names])
+        def my_func(env: ManagerBasedEnv, *args, **kwargs):
+
+    ..note:: The hook can access all the variables in the wrapped function's signature. While it is useful, the user should be careful to
+    access only existing variables.
+
+    Args:
+        _func: The function to decorate.
+        **descriptor_kwargs: Keyword arguments to pass to the descriptor.
+
+    Returns:
+        A decorator that can be used to decorate a function.
+    """
+    # If the decorator is used with a descriptor, use it as the descriptor.
+    if _func is not None and isinstance(_func, GenericObservationIODescriptor):
+        descriptor = _func
+        _func = None
+    else:
+        descriptor = _make_descriptor(**descriptor_kwargs)
+
+    # Ensures the hook is a list
+    if callable(on_inspect):
+        inspect_hooks: list[Callable[..., Any]] = [on_inspect]
+    else:
+        inspect_hooks: list[Callable[..., Any]] = list(on_inspect or [])  # handles None
+
+    def _apply(func: Callable[Concatenate[ManagerBasedEnv, P], R]) -> Callable[Concatenate[ManagerBasedEnv, P], R]:
+
+        # Capture the signature of the function
+        sig = inspect.signature(func)
+
+        @functools.wraps(func)
+        def wrapper(env: ManagerBasedEnv, *args: P.args, **kwargs: P.kwargs) -> R:
+            inspect_flag: bool = kwargs.pop("inspect", False)
+            out = func(env, *args, **kwargs)
+            if inspect_flag:
+                # Injects the function's arguments into the hooks and applies the defaults
+                bound = sig.bind(env, *args, **kwargs)
+                bound.apply_defaults()
+                call_kwargs = {
+                    "output": out,
+                    "descriptor": descriptor,
+                    **bound.arguments,
+                }
+                for hook in inspect_hooks:
+                    hook(**call_kwargs)
+            return out
+
+        # --- Descriptor bookkeeping ---
+        descriptor.name = func.__name__
+        descriptor.full_path = f"{func.__module__}.{func.__name__}"
+        descriptor.dtype = str(descriptor.dtype)
+        # Check if description is set in the descriptor
+        if descriptor.description is None:
+            descriptor.description = " ".join(func.__doc__.split())
+
+        # Adds the descriptor to the wrapped function as an attribute
+        wrapper._descriptor = descriptor
+        wrapper._has_descriptor = True
+        # Alters the signature of the wrapped function to make it match the original function.
+        # This allows the wrapped functions to pass the checks in the managers.
+        wrapper.__signature__ = sig
+        return wrapper
+
+    # If the decorator is used without parentheses, _func will be the function itself.
+    if callable(_func):
+        return _apply(_func)
+    return _apply
+
+
+def record_shape(output: torch.Tensor, descriptor: GenericObservationIODescriptor, **kwargs) -> None:
+    """Record the shape of the output tensor.
+
+    Args:
+        output: The output tensor.
+        descriptor: The descriptor to record the shape to.
+        **kwargs: Additional keyword arguments.
+    """
+    descriptor.shape = (output.shape[-1],)
+
+
+def record_dtype(output: torch.Tensor, descriptor: GenericObservationIODescriptor, **kwargs) -> None:
+    """Record the dtype of the output tensor.
+
+    Args:
+        output: The output tensor.
+        descriptor: The descriptor to record the dtype to.
+        **kwargs: Additional keyword arguments.
+    """
+    descriptor.dtype = str(output.dtype)
+
+
+def record_joint_names(output: torch.Tensor, descriptor: GenericObservationIODescriptor, **kwargs) -> None:
+    """Record the joint names of the output tensor.
+
+    Expects the `asset_cfg` keyword argument to be set.
+
+    Args:
+        output: The output tensor.
+        descriptor: The descriptor to record the joint names to.
+        **kwargs: Additional keyword arguments.
+    """
+    asset: Articulation = kwargs["env"].scene[kwargs["asset_cfg"].name]
+    joint_ids = kwargs["asset_cfg"].joint_ids
+    if joint_ids == slice(None, None, None):
+        joint_ids = list(range(len(asset.joint_names)))
+    descriptor.joint_names = [asset.joint_names[i] for i in joint_ids]
+
+
+def record_body_names(output: torch.Tensor, descriptor: GenericObservationIODescriptor, **kwargs) -> None:
+    """Record the body names of the output tensor.
+
+    Expects the `asset_cfg` keyword argument to be set.
+
+    Args:
+        output: The output tensor.
+        descriptor: The descriptor to record the body names to.
+        **kwargs: Additional keyword arguments.
+    """
+    asset: Articulation = kwargs["env"].scene[kwargs["asset_cfg"].name]
+    body_ids = kwargs["asset_cfg"].body_ids
+    if body_ids == slice(None, None, None):
+        body_ids = list(range(len(asset.body_names)))
+    descriptor.body_names = [asset.body_names[i] for i in body_ids]
+
+
+def record_joint_pos_offsets(output: torch.Tensor, descriptor: GenericObservationIODescriptor, **kwargs):
+    """Record the joint position offsets of the output tensor.
+
+    Expects the `asset_cfg` keyword argument to be set.
+
+    Args:
+        output: The output tensor.
+        descriptor: The descriptor to record the joint position offsets to.
+        **kwargs: Additional keyword arguments.
+    """
+    asset: Articulation = kwargs["env"].scene[kwargs["asset_cfg"].name]
+    ids = kwargs["asset_cfg"].joint_ids
+    # Get the offsets of the joints for the first robot in the scene.
+    # This assumes that all robots have the same joint offsets.
+    descriptor.joint_pos_offsets = asset.data.default_joint_pos[:, ids][0]
+
+
+def record_joint_vel_offsets(output: torch.Tensor, descriptor: GenericObservationIODescriptor, **kwargs):
+    """Record the joint velocity offsets of the output tensor.
+
+    Expects the `asset_cfg` keyword argument to be set.
+
+    Args:
+        output: The output tensor.
+        descriptor: The descriptor to record the joint velocity offsets to.
+        **kwargs: Additional keyword arguments.
+    """
+    asset: Articulation = kwargs["env"].scene[kwargs["asset_cfg"].name]
+    ids = kwargs["asset_cfg"].joint_ids
+    # Get the offsets of the joints for the first robot in the scene.
+    # This assumes that all robots have the same joint offsets.
+    descriptor.joint_vel_offsets = asset.data.default_joint_vel[:, ids][0]
+
+
+def export_articulations_data(env: ManagerBasedEnv) -> dict[str, dict[str, list[float]]]:
+    """Export the articulations data.
+
+    Args:
+        env: The environment.
+
+    Returns:
+        A dictionary containing the articulations data.
+    """
+    # Create a dictionary for all the articulations in the scene.
+    articulation_joint_data = {}
+    for articulation_name, articulation in env.scene.articulations.items():
+        # For each articulation, create a dictionary with the articulation's data.
+        # Some of the data may be redundant with other information provided by the observation descriptors.
+        articulation_joint_data[articulation_name] = {}
+        articulation_joint_data[articulation_name]["joint_names"] = articulation.joint_names
+        articulation_joint_data[articulation_name]["default_joint_pos"] = (
+            articulation.data.default_joint_pos[0].detach().cpu().numpy().tolist()
+        )
+        articulation_joint_data[articulation_name]["default_joint_vel"] = (
+            articulation.data.default_joint_vel[0].detach().cpu().numpy().tolist()
+        )
+        articulation_joint_data[articulation_name]["default_joint_pos_limits"] = (
+            articulation.data.default_joint_pos_limits[0].detach().cpu().numpy().tolist()
+        )
+        articulation_joint_data[articulation_name]["default_joint_damping"] = (
+            articulation.data.default_joint_damping[0].detach().cpu().numpy().tolist()
+        )
+        articulation_joint_data[articulation_name]["default_joint_stiffness"] = (
+            articulation.data.default_joint_stiffness[0].detach().cpu().numpy().tolist()
+        )
+        articulation_joint_data[articulation_name]["default_joint_friction"] = (
+            articulation.data.default_joint_friction[0].detach().cpu().numpy().tolist()
+        )
+        articulation_joint_data[articulation_name]["default_joint_armature"] = (
+            articulation.data.default_joint_armature[0].detach().cpu().numpy().tolist()
+        )
+    return articulation_joint_data
+
+
+def export_scene_data(env: ManagerBasedEnv) -> dict[str, Any]:
+    """Export the scene data.
+
+    Args:
+        env: The environment.
+
+    Returns:
+        A dictionary containing the scene data.
+    """
+    # Create a dictionary for the scene data.
+    scene_data = {"physics_dt": env.physics_dt, "dt": env.step_dt, "decimation": env.cfg.decimation}
+    return scene_data

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.
     """