Adds RayCaster rough terrain base height to reward (#1525)

# Description

Enhance `base_height_l2` function to support rough terrain adjustments:

- Added an optional `sensor_cfg` parameter to allow dynamic target
height
  adjustments based on sensor readings (e.g., RayCaster).
- Updated the function to calculate the L2 squared penalty using
adjusted
  height values for rough terrain scenarios.
- Preserved existing behavior for flat terrain by using the fixed
`target_height`.
- Improved compatibility for applications involving uneven terrain.

Fixes #1524

## Type of change

- New feature (non-breaking change which adds functionality)

## 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
- [x ] I have added tests that prove my fix is effective or that my
feature works
- [x ] 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: Kelly Guo <kellyg@nvidia.com>
Signed-off-by: Kelly Guo <kellyguo123@hotmail.com>
Co-authored-by: Kelly Guo <kellyg@nvidia.com>
Co-authored-by: Kelly Guo <kellyguo123@hotmail.com>

CONTRIBUTORS.md

@@ -49,6 +49,7 @@ Guidelines for modifications:
 * Giulio Romualdi
 * Haoran Zhou
 * HoJin Jeon
+* Hongwei Xiong
 * Jan Kerner
 * Jean Tampon
 * Jia Lin Yuan

source/extensions/omni.isaac.lab/config/extension.toml

 [package]
 
 # Note: Semantic Versioning is used: https://semver.org/
-version = "0.27.25"
+version = "0.27.26"
 
 # Description
 title = "Isaac Lab framework for Robot Learning"

source/extensions/omni.isaac.lab/docs/CHANGELOG.rst

 Changelog
 ---------
 
+0.27.26 (2024-12-11)
+~~~~~~~~~~~~~~~~~~~~
+
+Changed
+^^^^^^^
+
+* Introduced an optional ``sensor_cfg`` parameter to the :meth:`~omni.isaac.lab.envs.mdp.rewards.base_height_l2` function, enabling the use of
+  :class:`~omni.isaac.lab.sensors.RayCaster` for height adjustments. For flat terrains, the function retains its previous behavior.
+* Improved documentation to clarify the usage of the :meth:`~omni.isaac.lab.envs.mdp.rewards.base_height_l2` function in both flat and rough terrain settings.
+
+
 0.27.25 (2024-12-11)
 ~~~~~~~~~~~~~~~~~~~~
 

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/mdp/rewards.py

@@ -18,7 +18,7 @@ from omni.isaac.lab.assets import Articulation, RigidObject
 from omni.isaac.lab.managers import SceneEntityCfg
 from omni.isaac.lab.managers.manager_base import ManagerTermBase
 from omni.isaac.lab.managers.manager_term_cfg import RewardTermCfg
-from omni.isaac.lab.sensors import ContactSensor
+from omni.isaac.lab.sensors import ContactSensor, RayCaster
 
 if TYPE_CHECKING:
     from omni.isaac.lab.envs import ManagerBasedRLEnv
@@ -98,17 +98,28 @@ def flat_orientation_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = Scen
 
 
 def base_height_l2(
-    env: ManagerBasedRLEnv, target_height: float, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")
+    env: ManagerBasedRLEnv,
+    target_height: float,
+    asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
+    sensor_cfg: SceneEntityCfg | None = None,
 ) -> torch.Tensor:
     """Penalize asset height from its target using L2 squared kernel.
 
     Note:
-        Currently, it assumes a flat terrain, i.e. the target height is in the world frame.
+        For flat terrain, target height is in the world frame. For rough terrain,
+        sensor readings can adjust the target height to account for the terrain.
     """
     # extract the used quantities (to enable type-hinting)
     asset: RigidObject = env.scene[asset_cfg.name]
-    # TODO: Fix this for rough-terrain.
-    return torch.square(asset.data.root_pos_w[:, 2] - target_height)
+    if sensor_cfg is not None:
+        sensor: RayCaster = env.scene[sensor_cfg.name]
+        # Adjust the target height using the sensor data
+        adjusted_target_height = target_height + sensor.data.pos_w[:, 2]
+    else:
+        # Use the provided target height directly for flat terrain
+        adjusted_target_height = target_height
+    # Compute the L2 squared penalty
+    return torch.square(asset.data.root_pos_w[:, 2] - adjusted_target_height)
 
 
 def body_lin_acc_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor: