Adds preserve order flag to JointPositionToLimitsAction

source/isaaclab/config/extension.toml

@@ -1,7 +1,7 @@
 [package]
 
 # Note: Semantic Versioning is used: https://semver.org/
-version = "0.47.11"
+version = "0.47.12"
 
 # Description
 title = "Isaac Lab framework for Robot Learning"

source/isaaclab/docs/CHANGELOG.rst

@@ -1,6 +1,15 @@
 Changelog
 ---------
 
+0.47.12 (2025-10-15)
+~~~~~~~~~~~~~~~~~~~~
+
+Changed
+^^^^^^^
+
+* Add :attr:`preserve_order` flag to :class:`~isaaclab.envs.mdp.actions.actions_cfg.JointPositionToLimitsActionCfg`
+
+
 0.47.11 (2025-11-03)
 ~~~~~~~~~~~~~~~~~~~~
 

source/isaaclab/isaaclab/envs/mdp/actions/actions_cfg.py

@@ -131,6 +131,9 @@ class JointPositionToLimitsActionCfg(ActionTermCfg):
         This operation is performed after applying the scale factor.
     """
 
+    preserve_order: bool = False
+    """Whether to preserve the order of the joint names in the action output. Defaults to False."""
+
 
 @configclass
 class EMAJointPositionToLimitsActionCfg(JointPositionToLimitsActionCfg):

source/isaaclab/isaaclab/envs/mdp/actions/joint_actions_to_limits.py

@@ -53,7 +53,9 @@ def __init__(self, cfg: actions_cfg.JointPositionToLimitsActionCfg, env: Manager
         super().__init__(cfg, env)
 
         # resolve the joints over which the action term is applied
-        self._joint_ids, self._joint_names = self._asset.find_joints(self.cfg.joint_names)
+        self._joint_ids, self._joint_names = self._asset.find_joints(
+            self.cfg.joint_names, preserve_order=cfg.preserve_order
+        )
         self._num_joints = len(self._joint_ids)
         # log the resolved joint names for debugging
         omni.log.info(
@@ -75,17 +77,22 @@ def __init__(self, cfg: actions_cfg.JointPositionToLimitsActionCfg, env: Manager
         elif isinstance(cfg.scale, dict):
             self._scale = torch.ones(self.num_envs, self.action_dim, device=self.device)
             # resolve the dictionary config
-            index_list, _, value_list = string_utils.resolve_matching_names_values(self.cfg.scale, self._joint_names)
+            index_list, _, value_list = string_utils.resolve_matching_names_values(
+                self.cfg.scale, self._joint_names, preserve_order=cfg.preserve_order
+            )
             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 self.cfg.clip is not None:
             if isinstance(cfg.clip, dict):
                 self._clip = torch.tensor([[-float("inf"), float("inf")]], device=self.device).repeat(
                     self.num_envs, self.action_dim, 1
                 )
-                index_list, _, value_list = string_utils.resolve_matching_names_values(self.cfg.clip, self._joint_names)
+                index_list, _, value_list = string_utils.resolve_matching_names_values(
+                    self.cfg.clip, self._joint_names, preserve_order=cfg.preserve_order
+                )
                 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.")