Adds method to get a specific term from the action manager (#427)

# Description

Added option to get specific term by name from the action manager.

## Type of change

- New feature (non-breaking change which adds functionality)

## Checklist

- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./orbit.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
- [x] I have run all the tests with `./orbit.sh --test` and they pass
- [x] 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

source/extensions/omni.isaac.orbit/config/extension.toml

 [package]
 
 # Note: Semantic Versioning is used: https://semver.org/
-version = "0.12.0"
+version = "0.12.1"
 
 # Description
 title = "ORBIT framework for Robot Learning"

source/extensions/omni.isaac.orbit/docs/CHANGELOG.rst

 Changelog
 ---------
 
+0.12.1 (2024-03-09)
+~~~~~~~~~~~~~~~~~~~
+
+Added
+^^^^^
+
+* Added an option to the last actions observation term to get a specific term by name from the action manager.
+  If None, the behavior remains the same as before (the entire action is returned).
+
+
 0.12.0 (2024-03-08)
 ~~~~~~~~~~~~~~~~~~~
 

source/extensions/omni.isaac.orbit/omni/isaac/orbit/envs/mdp/observations.py

@@ -101,6 +101,7 @@ Sensors.
 
 def height_scan(env: BaseEnv, sensor_cfg: SceneEntityCfg, offset: float = 0.5) -> torch.Tensor:
     """Height scan from the given sensor w.r.t. the sensor's frame.
+
     The provided offset (Defaults to 0.5) is subtracted from the returned values.
     """
     # extract the used quantities (to enable type-hinting)
@@ -126,9 +127,16 @@ Actions.
 """
 
 
-def last_action(env: BaseEnv) -> torch.Tensor:
-    """The last input action to the environment."""
-    return env.action_manager.action
+def last_action(env: BaseEnv, action_name: str | None = None) -> torch.Tensor:
+    """The last input action to the environment.
+
+    The name of the action term for which the action is required. If None, the
+    entire action tensor is returned.
+    """
+    if action_name is None:
+        return env.action_manager.action
+    else:
+        return env.action_manager.get_term(action_name).raw_actions
 
 
 """

source/extensions/omni.isaac.orbit/omni/isaac/orbit/managers/action_manager.py

@@ -134,7 +134,7 @@ class ActionManager(ManagerBase):
         table.align["Name"] = "l"
         table.align["Dimension"] = "r"
         # add info on each term
-        for index, (name, term) in enumerate(zip(self._term_names, self._terms)):
+        for index, (name, term) in enumerate(self._terms.items()):
             table.add_row([index, name, term.action_dim])
         # convert table to string
         msg += table.get_string()
@@ -159,7 +159,7 @@ class ActionManager(ManagerBase):
     @property
     def action_term_dim(self) -> list[int]:
         """Shape of each action term."""
-        return [term.action_dim for term in self._terms]
+        return [term.action_dim for term in self._terms.values()]
 
     @property
     def action(self) -> torch.Tensor:
@@ -192,7 +192,7 @@ class ActionManager(ManagerBase):
         self._prev_action[env_ids] = 0.0
         self._action[env_ids] = 0.0
         # reset all action terms
-        for term in self._terms:
+        for term in self._terms.values():
             term.reset(env_ids=env_ids)
         # nothing to log here
         return {}
@@ -215,7 +215,7 @@ class ActionManager(ManagerBase):
 
         # split the actions and apply to each tensor
         idx = 0
-        for term in self._terms:
+        for term in self._terms.values():
             term_actions = action[:, idx : idx + term.action_dim]
             term.process_actions(term_actions)
             idx += term.action_dim
@@ -226,9 +226,20 @@ class ActionManager(ManagerBase):
         Note:
             This should be called at every simulation step.
         """
-        for term in self._terms:
+        for term in self._terms.values():
             term.apply_actions()
 
+    def get_term(self, name: str) -> ActionTerm:
+        """Returns the action term with the specified name.
+
+        Args:
+            name: The name of the action term.
+
+        Returns:
+            The action term with the specified name.
+        """
+        return self._terms[name]
+
     """
     Helper functions.
     """
@@ -237,7 +248,7 @@ class ActionManager(ManagerBase):
         """Prepares a list of action terms."""
         # parse action terms from the config
         self._term_names: list[str] = list()
-        self._terms: list[ActionTerm] = list()
+        self._terms: dict[str, ActionTerm] = dict()
 
         # check if config is dict already
         if isinstance(self.cfg, dict):
@@ -261,4 +272,4 @@ class ActionManager(ManagerBase):
                 raise TypeError(f"Returned object for the term '{term_name}' is not of type ActionType.")
             # add term name and parameters
             self._term_names.append(term_name)
-            self._terms.append(term)
+            self._terms[term_name] = term