preprocess in init

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/mdp/actions/binary_joint_actions.py

@@ -42,7 +42,7 @@ class BinaryJointAction(ActionTerm):
     """The configuration of the action term."""
     _asset: Articulation
     """The articulation asset on which the action term is applied."""
-    _clip: dict[str, tuple] | None = None
+    _clip: torch.Tensor
     """The clip applied to the input action."""
 
     def __init__(self, cfg: actions_cfg.BinaryJointActionCfg, env: ManagerBasedEnv) -> None:
@@ -85,9 +85,11 @@ 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 self.cfg.clip is not None:
             if isinstance(cfg.clip, dict):
-                self._clip = cfg.clip
+                self._clip = torch.tensor([[-float('inf'), float('inf')]], device=self.device).expand(self.num_envs, self.action_dim, 2).clone()
+                index_list, _, value_list = string_utils.resolve_matching_names_values(self.cfg.clip, self._joint_names)
+                self._clip[:, index_list] = torch.tensor(value_list, device=self.device)
             else:
                 raise ValueError(f"Unsupported clip type: {type(cfg.clip)}. Supported types are dict.")
 
@@ -123,13 +125,8 @@ 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
-            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)
+        if self.cfg.clip is not None:
+            self._processed_actions = torch.clamp(self._processed_actions, min=self._clip[:, :, 0], max=self._clip[:, :, 1])
 
     def reset(self, env_ids: Sequence[int] | None = None) -> None:
         self._raw_actions[env_ids] = 0.0

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/mdp/actions/joint_actions.py

@@ -50,7 +50,7 @@ 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
+    _clip: torch.Tensor
     """The clip applied to the input action."""
 
     def __init__(self, cfg: actions_cfg.JointActionCfg, env: ManagerBasedEnv) -> None:
@@ -97,9 +97,11 @@ def __init__(self, cfg: actions_cfg.JointActionCfg, env: ManagerBasedEnv) -> Non
         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 self.cfg.clip is not None:
             if isinstance(cfg.clip, dict):
-                self._clip = cfg.clip
+                self._clip = torch.tensor([[-float('inf'), float('inf')]], device=self.device).expand(self.num_envs, self.action_dim, 2).clone()
+                index_list, _, value_list = string_utils.resolve_matching_names_values(self.cfg.clip, self._joint_names)
+                self._clip[:, index_list] = torch.tensor(value_list, device=self.device)
             else:
                 raise ValueError(f"Unsupported clip type: {type(cfg.clip)}. Supported types are dict.")
 
@@ -129,12 +131,8 @@ def process_actions(self, actions: torch.Tensor):
         # 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)
+        if self.cfg.clip is not None:
+            self._processed_actions = torch.clamp(self._processed_actions, min=self._clip[:, :, 0], max=self._clip[:, :, 1])
 
     def reset(self, env_ids: Sequence[int] | None = None) -> None:
         self._raw_actions[env_ids] = 0.0

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/mdp/actions/joint_actions_to_limits.py

@@ -44,7 +44,7 @@ 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
+    _clip: torch.Tensor
     """The clip applied to the input action."""
 
     def __init__(self, cfg: actions_cfg.JointPositionToLimitsActionCfg, env: ManagerBasedEnv):
@@ -79,9 +79,11 @@ def __init__(self, cfg: actions_cfg.JointPositionToLimitsActionCfg, env: Manager
         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 self.cfg.clip is not None:
             if isinstance(cfg.clip, dict):
-                self._clip = cfg.clip
+                self._clip = torch.tensor([[-float('inf'), float('inf')]], device=self.device).expand(self.num_envs, self.action_dim, 2).clone()
+                index_list, _, value_list = string_utils.resolve_matching_names_values(self.cfg.clip, self._joint_names)
+                self._clip[:, index_list] = torch.tensor(value_list, device=self.device)
             else:
                 raise ValueError(f"Unsupported clip type: {type(cfg.clip)}. Supported types are dict.")
 
@@ -110,13 +112,8 @@ 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)
+        if self.cfg.clip is not None:
+            self._processed_actions = torch.clamp(self._processed_actions, min=self._clip[:, :, 0], max=self._clip[:, :, 1])
         # 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:

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/mdp/actions/non_holonomic_actions.py

@@ -60,7 +60,7 @@ 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
+    _clip: torch.Tensor
     """The clip applied to the input action."""
 
     def __init__(self, cfg: actions_cfg.NonHolonomicActionCfg, env: ManagerBasedEnv):
@@ -108,9 +108,11 @@ def __init__(self, cfg: actions_cfg.NonHolonomicActionCfg, env: ManagerBasedEnv)
         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 self.cfg.clip is not None:
             if isinstance(cfg.clip, dict):
-                self._clip = cfg.clip
+                self._clip = torch.tensor([[-float('inf'), float('inf')]], device=self.device).expand(self.num_envs, self.action_dim, 2).clone()
+                index_list, _, value_list = string_utils.resolve_matching_names_values(self.cfg.clip, self._joint_names)
+                self._clip[:, index_list] = torch.tensor(value_list, device=self.device)
             else:
                 raise ValueError(f"Unsupported clip type: {type(cfg.clip)}. Supported types are dict.")
 
@@ -139,12 +141,8 @@ def process_actions(self, 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)
+        if self.cfg.clip is not None:
+            self._processed_actions = torch.clamp(self._processed_actions, min=self._clip[:, :, 0], max=self._clip[:, :, 1])
 
     def apply_actions(self):
         # obtain current heading

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/mdp/actions/task_space_actions.py

@@ -43,7 +43,7 @@ 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
+    _clip: torch.Tensor
     """The clip applied to the input action."""
 
     def __init__(self, cfg: actions_cfg.DifferentialInverseKinematicsActionCfg, env: ManagerBasedEnv):
@@ -105,9 +105,11 @@ def __init__(self, cfg: actions_cfg.DifferentialInverseKinematicsActionCfg, env:
             self._offset_pos, self._offset_rot = None, None
 
         # parse clip
-        if cfg.clip is not None:
+        if self.cfg.clip is not None:
             if isinstance(cfg.clip, dict):
-                self._clip = cfg.clip
+                self._clip = torch.tensor([[-float('inf'), float('inf')]], device=self.device).expand(self.num_envs, self.action_dim, 2).clone()
+                index_list, _, value_list = string_utils.resolve_matching_names_values(self.cfg.clip, self._joint_names)
+                self._clip[:, index_list] = torch.tensor(value_list, device=self.device)
             else:
                 raise ValueError(f"Unsupported clip type: {type(cfg.clip)}. Supported types are dict.")
 
@@ -135,13 +137,8 @@ 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)
+        if self.cfg.clip is not None:
+            self._processed_actions = torch.clamp(self._processed_actions, min=self._clip[:, :, 0], max=self._clip[:, :, 1])
         # obtain quantities from simulation
         ee_pos_curr, ee_quat_curr = self._compute_frame_pose()
         # set command into controller