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Fixes docstring in reward functions and code block in documentation #661

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merged 4 commits into from
Jul 17, 2024
Merged

Fixes docstring in reward functions and code block in documentation #661

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3b61483
Fixes slash in the sample documentation
fan-ziqi Jul 6, 2024
7c0b316
Fixes norm description of joint_acc_l2
fan-ziqi Jul 9, 2024
4a2703e
Update docstring in mdp rewards
Dhoeller19 Jul 17, 2024
1fcdeba
Merge branch 'main' into devel-docs
Dhoeller19 Jul 17, 2024
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2 changes: 1 addition & 1 deletion docs/source/setup/sample.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -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
~~~~~~~~~~~~~~~~~~~~~~
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24 changes: 12 additions & 12 deletions source/extensions/omni.isaac.lab/omni/isaac/lab/envs/mdp/rewards.py
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Expand Up @@ -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.
"""
Expand All @@ -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.
Expand All @@ -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]
Expand All @@ -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]
Expand Down Expand Up @@ -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)


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