Introducing new feature for hydra group configuration override.

Author: ooctipus

docs/source/features/hydra.rst

@@ -127,3 +127,137 @@ the post init update is as follows:
 
 Here, when modifying ``env.decimation`` or ``env.sim.dt``, the user needs to give the updated ``env.sim.render_interval``,
 ``env.scene.height_scanner.update_period``, and ``env.scene.contact_forces.update_period`` as input as well.
+
+
+Group Override
+--------------
+Group override lets you swap out entire groups of environment- or agent-level settings in one go.
+Instead of overriding individual fields, you select a named preset defined in your code.
+
+
+Group Presets
+^^^^^^^^^^^^^
+First define the available group override options
+
+
+.. code-block:: python
+
+    @configclass
+    class StateNoNoiseObservationsCfg:
+        """Observation specifications for the MDP."""
+
+        @configclass
+        class PolicyCfg(ObsGroup):
+            """Observations for policy group."""
+
+            # observation terms (order preserved)
+            joint_pos = ObsTerm(func=mdp.joint_pos_rel)
+            # other terms .......
+
+            def __post_init__(self):
+                self.enable_corruption = False
+                self.concatenate_terms = True
+
+        # observation groups
+        policy: PolicyCfg = PolicyCfg()
+
+
+    @configclass
+    class EnvConfigurables:
+        env: dict[str, any] = {
+            "observations": {
+                "state_obs_no_noise": StateNoNoiseObservationsCfg(),
+                "state_obs_noisy": # other option,
+            },
+            "actions.arm_action": {
+                "joint_pos_arm_action": mdp.JointPositionActionCfg(
+                    asset_name="robot", joint_names=["panda_joint.*"], scale=0.5, use_default_offset=True
+                ),
+                "osc_arm_action": mdp.OperationalSpaceControllerActionCfg(
+                    asset_name="robot",
+                    # rest of fields
+                ),
+            },
+            "events": {
+                "rand_joint_pos_friction": JointRandPositionFrictionEventCfg(),
+                "rand_joint_pos_friction_amarture": JointRandPositionFrictionAmartureEventCfg(),
+            },
+            "events.reset_robot_joints": {
+                "aggressive": EventTerm(
+                    func=mdp.reset_joints_by_scale,
+                    mode="reset",
+                    params={
+                        "position_range": (0.0, 2.0),
+                        "velocity_range": (0.0, 1.0),
+                    },
+                ),
+                "easy": # easy EventTerm with narrower ranges
+            },
+        }
+
+
+
+    @configclass
+    class AgentConfigurables(EnvConfigurables):
+        agent: dict[str, any] = {
+            "policy": {
+                "large_network": RslRlPpoActorCriticCfg(
+                    init_noise_std=1.0,
+                    actor_hidden_dims=[512, 256, 128, 64],
+                    critic_hidden_dims=[512, 256, 128, 64],
+                    activation="elu",
+                ),
+                "medium_network": RslRlPpoActorCriticCfg(
+                    init_noise_std=1.0,
+                    actor_hidden_dims=[256, 128, 64],
+                    critic_hidden_dims=[256, 128, 64],
+                    activation="elu",
+                ),
+                "small_network": RslRlPpoActorCriticCfg(
+                    init_noise_std=1.0,
+                    actor_hidden_dims=[128, 64],
+                    critic_hidden_dims=[128, 64],
+                    activation="elu",
+                ),
+            },
+            # algorithm cfg.....
+        }
+
+
+Group Registration
+^^^^^^^^^^^^^^^^^^
+When you register your Gym environment, provide the ``configurable_entry_point`` pointing to your ``@configclass``:
+
+.. code-block:: python
+
+    gym.register(
+        id="Isaac-Reach-Franka-v0",
+        entry_point="isaaclab.envs:ManagerBasedRLEnv",
+        disable_env_checker=True,
+        kwargs={
+            # … other cfg entry points …
+            "configurable_entry_point": f"{agents.__name__}.configurables:AgentConfigurables"
+        },
+    )
+
+
+Override Syntax
+^^^^^^^^^^^^^^^
+Select one preset per group via Hydra-style CLI flags. For example::
+
+    python scripts/reinforcement_learning/rsl_rl/train.py \
+      --task=Isaac-Reach-Franka-v0 \
+      --headless \
+      env.events=rand_joint_pos_friction_amarture \
+      env.observations=state_obs_no_noise  \
+      env.actions.arm_action=osc_arm_action    \
+      agent.policy=large_network
+
+Under the hood, Hydra will replace:
+
+- ``env.events``             with ``EnvConfigurables.env["rand_joint_pos_friction_amarture"]``
+- ``env.observations``       with ``EnvConfigurables.env["state_obs_no_noise"]``
+- ``env.actions.arm_action`` with ``EnvConfigurables.env["actions.arm_action"]["osc_arm_action"]``
+- ``agent.policy``           with ``AgentConfigurables.agent["large_network"]``
+
+allowing you to switch qualitative modes of your experiments with a single flag.

source/isaaclab_tasks/config/extension.toml

@@ -1,7 +1,7 @@
 [package]
 
 # Note: Semantic Versioning is used: https://semver.org/
-version = "0.10.36"
+version = "0.11.0"
 
 # Description
 title = "Isaac Lab Environments"

source/isaaclab_tasks/docs/CHANGELOG.rst

@@ -1,6 +1,15 @@
 Changelog
 ---------
 
+0.11.0 (2025-07-05)
+~~~~~~~~~~~~~~~~~~~~
+
+Changed
+^^^^^^^
+
+* Add new feature that support hydra group config override, and provide example at Isaac-Reach-Franka-v0 env
+
+
 0.10.36 (2025-06-26)
 ~~~~~~~~~~~~~~~~~~~~
 

source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/__init__.py

@@ -24,6 +24,7 @@
         "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml",
         "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:FrankaReachPPORunnerCfg",
         "skrl_cfg_entry_point": f"{agents.__name__}:skrl_ppo_cfg.yaml",
+        "configurable_entry_point": f"{agents.__name__}.configurables:AgentConfigurables",
     },
 )
 

source/isaaclab_tasks/isaaclab_tasks/manager_based/manipulation/reach/config/franka/agents/configurables.py

@@ -0,0 +1,66 @@
+# 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 isaaclab.utils import configclass
+
+from isaaclab_rl.rsl_rl import RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg
+
+from ..configurables import EnvConfigurables
+
+
+@configclass
+class AgentConfigurables(EnvConfigurables):
+    agent: dict[str, any] = {
+        "policy": {
+            "large_network": RslRlPpoActorCriticCfg(
+                init_noise_std=1.0,
+                actor_hidden_dims=[512, 256, 128, 64],
+                critic_hidden_dims=[512, 256, 128, 64],
+                activation="elu",
+            ),
+            "medium_network": RslRlPpoActorCriticCfg(
+                init_noise_std=1.0,
+                actor_hidden_dims=[256, 128, 64],
+                critic_hidden_dims=[256, 128, 64],
+                activation="elu",
+            ),
+            "small_network": RslRlPpoActorCriticCfg(
+                init_noise_std=1.0,
+                actor_hidden_dims=[128, 64],
+                critic_hidden_dims=[128, 64],
+                activation="elu",
+            ),
+        },
+        "algorithm": {
+            "standard": RslRlPpoAlgorithmCfg(
+                value_loss_coef=1.0,
+                use_clipped_value_loss=True,
+                clip_param=0.2,
+                entropy_coef=0.001,
+                num_learning_epochs=8,
+                num_mini_batches=4,
+                learning_rate=1.0e-3,
+                schedule="adaptive",
+                gamma=0.99,
+                lam=0.95,
+                desired_kl=0.01,
+                max_grad_norm=1.0,
+            ),
+            "small_batch": RslRlPpoAlgorithmCfg(
+                value_loss_coef=1.0,
+                use_clipped_value_loss=True,
+                clip_param=0.2,
+                entropy_coef=0.001,
+                num_learning_epochs=8,
+                num_mini_batches=16,
+                learning_rate=1.0e-4,
+                schedule="adaptive",
+                gamma=0.99,
+                lam=0.95,
+                desired_kl=0.01,
+                max_grad_norm=1.0,
+            ),
+        },
+    }