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feat: add clip range of JointAction #1476

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feat: add clip range of JointAction #1476

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1238d7a
feat: add clip range of JointAction
fan-ziqi Nov 28, 2024
bab210d
feat: add clip-cfg to ActionTermCfg && add clip to all mdp/actions
fan-ziqi Dec 7, 2024
7f98385
fix: del debug print
fan-ziqi Dec 7, 2024
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17 changes: 16 additions & 1 deletion source/extensions/omni.isaac.lab/omni/isaac/lab/envs/mdp/actions/binary_joint_actions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,9 +40,10 @@ class BinaryJointAction(ActionTerm):

cfg: actions_cfg.BinaryJointActionCfg
"""The configuration of the action term."""

_asset: Articulation
"""The articulation asset on which the action term is applied."""
_clip: dict[str, tuple] | None = None
"""The clip applied to the input action."""

def __init__(self, cfg: actions_cfg.BinaryJointActionCfg, env: ManagerBasedEnv) -> None:
# initialize the action term
Expand Down Expand Up @@ -83,6 +84,13 @@ def __init__(self, cfg: actions_cfg.BinaryJointActionCfg, env: ManagerBasedEnv)
)
self._close_command[index_list] = torch.tensor(value_list, device=self.device)

# parse clip
if cfg.clip is not None:
if isinstance(cfg.clip, dict):
self._clip = cfg.clip
else:
raise ValueError(f"Unsupported clip type: {type(cfg.clip)}. Supported types are dict.")

"""
Properties.
"""
Expand Down Expand Up @@ -115,6 +123,13 @@ def process_actions(self, actions: torch.Tensor):
binary_mask = actions < 0
# compute the command
self._processed_actions = torch.where(binary_mask, self._close_command, self._open_command)
# clip actions
if self._clip is not None:
# resolve the dictionary config
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is it possible to resolve this once at initialization and convert to a torch tensor to avoid more overhead with the loop?

index_list, _, value_list = string_utils.resolve_matching_names_values(self._clip, self._joint_names)
for index in range(len(index_list)):
min_value, max_value = value_list[index]
self._processed_actions[:, index_list[index]].clip_(min_value, max_value)

def reset(self, env_ids: Sequence[int] | None = None) -> None:
self._raw_actions[env_ids] = 0.0
Expand Down
15 changes: 15 additions & 0 deletions source/extensions/omni.isaac.lab/omni/isaac/lab/envs/mdp/actions/joint_actions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,6 +50,8 @@ class JointAction(ActionTerm):
"""The scaling factor applied to the input action."""
_offset: torch.Tensor | float
"""The offset applied to the input action."""
_clip: dict[str, tuple] | None = None
"""The clip applied to the input action."""

def __init__(self, cfg: actions_cfg.JointActionCfg, env: ManagerBasedEnv) -> None:
# initialize the action term
Expand Down Expand Up @@ -94,6 +96,12 @@ def __init__(self, cfg: actions_cfg.JointActionCfg, env: ManagerBasedEnv) -> Non
self._offset[:, index_list] = torch.tensor(value_list, device=self.device)
else:
raise ValueError(f"Unsupported offset type: {type(cfg.offset)}. Supported types are float and dict.")
# parse clip
if cfg.clip is not None:
if isinstance(cfg.clip, dict):
self._clip = cfg.clip
else:
raise ValueError(f"Unsupported clip type: {type(cfg.clip)}. Supported types are dict.")

"""
Properties.
Expand All @@ -120,6 +128,13 @@ def process_actions(self, actions: torch.Tensor):
self._raw_actions[:] = actions
# apply the affine transformations
self._processed_actions = self._raw_actions * self._scale + self._offset
# clip actions
if self._clip is not None:
# resolve the dictionary config
index_list, _, value_list = string_utils.resolve_matching_names_values(self._clip, self._joint_names)
for index in range(len(index_list)):
min_value, max_value = value_list[index]
self._processed_actions[:, index_list[index]].clip_(min_value, max_value)

def reset(self, env_ids: Sequence[int] | None = None) -> None:
self._raw_actions[env_ids] = 0.0
Expand Down
15 changes: 15 additions & 0 deletions source/extensions/omni.isaac.lab/omni/isaac/lab/envs/mdp/actions/joint_actions_to_limits.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,6 +44,8 @@ class JointPositionToLimitsAction(ActionTerm):
"""The articulation asset on which the action term is applied."""
_scale: torch.Tensor | float
"""The scaling factor applied to the input action."""
_clip: dict[str, tuple] | None = None
"""The clip applied to the input action."""

def __init__(self, cfg: actions_cfg.JointPositionToLimitsActionCfg, env: ManagerBasedEnv):
# initialize the action term
Expand Down Expand Up @@ -76,6 +78,12 @@ def __init__(self, cfg: actions_cfg.JointPositionToLimitsActionCfg, env: Manager
self._scale[:, index_list] = torch.tensor(value_list, device=self.device)
else:
raise ValueError(f"Unsupported scale type: {type(cfg.scale)}. Supported types are float and dict.")
# parse clip
if cfg.clip is not None:
if isinstance(cfg.clip, dict):
self._clip = cfg.clip
else:
raise ValueError(f"Unsupported clip type: {type(cfg.clip)}. Supported types are dict.")

"""
Properties.
Expand All @@ -102,6 +110,13 @@ def process_actions(self, actions: torch.Tensor):
self._raw_actions[:] = actions
# apply affine transformations
self._processed_actions = self._raw_actions * self._scale
# clip actions
if self._clip is not None:
# resolve the dictionary config
index_list, _, value_list = string_utils.resolve_matching_names_values(self._clip, self._joint_names)
for index in range(len(index_list)):
min_value, max_value = value_list[index]
self._processed_actions[:, index_list[index]].clip_(min_value, max_value)
# rescale the position targets if configured
# this is useful when the input actions are in the range [-1, 1]
if self.cfg.rescale_to_limits:
Expand Down
16 changes: 16 additions & 0 deletions source/extensions/omni.isaac.lab/omni/isaac/lab/envs/mdp/actions/non_holonomic_actions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@

import omni.log

import omni.isaac.lab.utils.string as string_utils
from omni.isaac.lab.assets.articulation import Articulation
from omni.isaac.lab.managers.action_manager import ActionTerm
from omni.isaac.lab.utils.math import euler_xyz_from_quat
Expand Down Expand Up @@ -59,6 +60,8 @@ class NonHolonomicAction(ActionTerm):
"""The scaling factor applied to the input action. Shape is (1, 2)."""
_offset: torch.Tensor
"""The offset applied to the input action. Shape is (1, 2)."""
_clip: dict[str, tuple] | None = None
"""The clip applied to the input action."""

def __init__(self, cfg: actions_cfg.NonHolonomicActionCfg, env: ManagerBasedEnv):
# initialize the action term
Expand Down Expand Up @@ -104,6 +107,12 @@ def __init__(self, cfg: actions_cfg.NonHolonomicActionCfg, env: ManagerBasedEnv)
# save the scale and offset as tensors
self._scale = torch.tensor(self.cfg.scale, device=self.device).unsqueeze(0)
self._offset = torch.tensor(self.cfg.offset, device=self.device).unsqueeze(0)
# parse clip
if cfg.clip is not None:
if isinstance(cfg.clip, dict):
self._clip = cfg.clip
else:
raise ValueError(f"Unsupported clip type: {type(cfg.clip)}. Supported types are dict.")

"""
Properties.
Expand All @@ -129,6 +138,13 @@ def process_actions(self, actions):
# store the raw actions
self._raw_actions[:] = actions
self._processed_actions = self.raw_actions * self._scale + self._offset
# clip actions
if self._clip is not None:
# resolve the dictionary config
index_list, _, value_list = string_utils.resolve_matching_names_values(self._clip, self._joint_names)
for index in range(len(index_list)):
min_value, max_value = value_list[index]
self._processed_actions[:, index_list[index]].clip_(min_value, max_value)

def apply_actions(self):
# obtain current heading
Expand Down
17 changes: 17 additions & 0 deletions source/extensions/omni.isaac.lab/omni/isaac/lab/envs/mdp/actions/task_space_actions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@
import omni.log

import omni.isaac.lab.utils.math as math_utils
import omni.isaac.lab.utils.string as string_utils
from omni.isaac.lab.assets.articulation import Articulation
from omni.isaac.lab.controllers.differential_ik import DifferentialIKController
from omni.isaac.lab.managers.action_manager import ActionTerm
Expand Down Expand Up @@ -42,6 +43,8 @@ class DifferentialInverseKinematicsAction(ActionTerm):
"""The articulation asset on which the action term is applied."""
_scale: torch.Tensor
"""The scaling factor applied to the input action. Shape is (1, action_dim)."""
_clip: dict[str, tuple] | None = None
"""The clip applied to the input action."""

def __init__(self, cfg: actions_cfg.DifferentialInverseKinematicsActionCfg, env: ManagerBasedEnv):
# initialize the action term
Expand Down Expand Up @@ -101,6 +104,13 @@ def __init__(self, cfg: actions_cfg.DifferentialInverseKinematicsActionCfg, env:
else:
self._offset_pos, self._offset_rot = None, None

# parse clip
if cfg.clip is not None:
if isinstance(cfg.clip, dict):
self._clip = cfg.clip
else:
raise ValueError(f"Unsupported clip type: {type(cfg.clip)}. Supported types are dict.")

"""
Properties.
"""
Expand All @@ -125,6 +135,13 @@ def process_actions(self, actions: torch.Tensor):
# store the raw actions
self._raw_actions[:] = actions
self._processed_actions[:] = self.raw_actions * self._scale
# clip actions
if self._clip is not None:
# resolve the dictionary config
index_list, _, value_list = string_utils.resolve_matching_names_values(self._clip, self._joint_names)
for index in range(len(index_list)):
min_value, max_value = value_list[index]
self._processed_actions[:, index_list[index]].clip_(min_value, max_value)
# obtain quantities from simulation
ee_pos_curr, ee_quat_curr = self._compute_frame_pose()
# set command into controller
Expand Down
3 changes: 3 additions & 0 deletions source/extensions/omni.isaac.lab/omni/isaac/lab/managers/manager_term_cfg.py
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Expand Up @@ -76,6 +76,9 @@ class for more details.
debug_vis: bool = False
"""Whether to visualize debug information. Defaults to False."""

clip: dict[str, tuple] | None = None
"""Clip range for the action (dict of regex expressions). Defaults to None."""


##
# Command manager.
Expand Down