Fixes docstring in reward functions and code block in documentation (i…

…saac-sim#661)

# Description

Fixes docstring in reward functions and code block in documentation

## Type of change

- Bug fix

## 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

docs/source/setup/sample.rst

@@ -165,7 +165,7 @@ format.
 
    .. code:: bash
 
-      ./isaaclab.sh -p source/standalone//workflows/robomimic/play.py --task Isaac-Lift-Cube-Franka-IK-Rel-v0 --checkpoint /PATH/TO/model.pth
+      ./isaaclab.sh -p source/standalone/workflows/robomimic/play.py --task Isaac-Lift-Cube-Franka-IK-Rel-v0 --checkpoint /PATH/TO/model.pth
 
 Reinforcement Learning
 ~~~~~~~~~~~~~~~~~~~~~~

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

@@ -74,21 +74,21 @@ def __call__(self, env: ManagerBasedRLEnv, term_keys: str | list[str] = ".*") ->
 
 
 def lin_vel_z_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
-    """Penalize z-axis base linear velocity using L2-kernel."""
+    """Penalize z-axis base linear velocity using L2 squared kernel."""
     # extract the used quantities (to enable type-hinting)
     asset: RigidObject = env.scene[asset_cfg.name]
     return torch.square(asset.data.root_lin_vel_b[:, 2])
 
 
 def ang_vel_xy_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
-    """Penalize xy-axis base angular velocity using L2-kernel."""
+    """Penalize xy-axis base angular velocity using L2 squared kernel."""
     # extract the used quantities (to enable type-hinting)
     asset: RigidObject = env.scene[asset_cfg.name]
     return torch.sum(torch.square(asset.data.root_ang_vel_b[:, :2]), dim=1)
 
 
 def flat_orientation_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
-    """Penalize non-flat base orientation using L2-kernel.
+    """Penalize non-flat base orientation using L2 squared kernel.
 
     This is computed by penalizing the xy-components of the projected gravity vector.
     """
@@ -100,7 +100,7 @@ def flat_orientation_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = Scen
 def base_height_l2(
     env: ManagerBasedRLEnv, target_height: float, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")
 ) -> torch.Tensor:
-    """Penalize asset height from its target using L2-kernel.
+    """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.
@@ -123,9 +123,9 @@ def body_lin_acc_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEnt
 
 
 def joint_torques_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
-    """Penalize joint torques applied on the articulation using L2-kernel.
+    """Penalize joint torques applied on the articulation using L2 squared kernel.
 
-    NOTE: Only the joints configured in :attr:`asset_cfg.joint_ids` will have their joint torques contribute to the L2 norm.
+    NOTE: Only the joints configured in :attr:`asset_cfg.joint_ids` will have their joint torques contribute to the term.
     """
     # extract the used quantities (to enable type-hinting)
     asset: Articulation = env.scene[asset_cfg.name]
@@ -140,19 +140,19 @@ def joint_vel_l1(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg) -> torch.Ten
 
 
 def joint_vel_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
-    """Penalize joint velocities on the articulation using L1-kernel.
+    """Penalize joint velocities on the articulation using L2 squared kernel.
 
-    NOTE: Only the joints configured in :attr:`asset_cfg.joint_ids` will have their joint velocities contribute to the L1 norm.
+    NOTE: Only the joints configured in :attr:`asset_cfg.joint_ids` will have their joint velocities contribute to the term.
     """
     # extract the used quantities (to enable type-hinting)
     asset: Articulation = env.scene[asset_cfg.name]
     return torch.sum(torch.square(asset.data.joint_vel[:, asset_cfg.joint_ids]), dim=1)
 
 
 def joint_acc_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
-    """Penalize joint accelerations on the articulation using L2-kernel.
+    """Penalize joint accelerations on the articulation using L2 squared kernel.
 
-    NOTE: Only the joints configured in :attr:`asset_cfg.joint_ids` will have their joint accelerations contribute to the L2 norm.
+    NOTE: Only the joints configured in :attr:`asset_cfg.joint_ids` will have their joint accelerations contribute to the term.
     """
     # extract the used quantities (to enable type-hinting)
     asset: Articulation = env.scene[asset_cfg.name]
@@ -232,12 +232,12 @@ def applied_torque_limits(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = Sc
 
 
 def action_rate_l2(env: ManagerBasedRLEnv) -> torch.Tensor:
-    """Penalize the rate of change of the actions using L2-kernel."""
+    """Penalize the rate of change of the actions using L2 squared kernel."""
     return torch.sum(torch.square(env.action_manager.action - env.action_manager.prev_action), dim=1)
 
 
 def action_l2(env: ManagerBasedRLEnv) -> torch.Tensor:
-    """Penalize the actions using L2-kernel."""
+    """Penalize the actions using L2 squared kernel."""
     return torch.sum(torch.square(env.action_manager.action), dim=1)