Expands observation term scaling to support list of floats

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

@@ -1,7 +1,7 @@
 [package]
 
 # Note: Semantic Versioning is used: https://semver.org/
-version = "0.25.2"
+version = "0.25.3"
 
 # Description
 title = "Isaac Lab framework for Robot Learning"

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

@@ -1,6 +1,17 @@
 Changelog
 ---------
 
+
+0.25.3 (2024-10-18)
+~~~~~~~~~~~~~~~~~~~~
+
+Added
+^^^^^
+
+* Added support to define tuple of floats to scale observation terms by expanding the
+  :attr:`omni.isaac.lab.managers.manager_term_cfg.ObservationManagerCfg.scale` attribute.
+
+
 0.25.2 (2024-10-16)
 ~~~~~~~~~~~~~~~~~~~~
 

source/extensions/omni.isaac.lab/omni/isaac/lab/managers/manager_term_cfg.py

@@ -152,7 +152,7 @@ class ObservationTermCfg(ManagerTermBaseCfg):
     """The clipping range for the observation after adding noise. Defaults to None,
     in which case no clipping is applied."""
 
-    scale: float | None = None
+    scale: tuple[float, ...] | float | None = None
     """The scale to apply to the observation after clipping. Defaults to None,
     in which case no scaling is applied (same as setting scale to :obj:`1`)."""
 

source/extensions/omni.isaac.lab/omni/isaac/lab/managers/observation_manager.py

@@ -253,7 +253,7 @@ def compute_group(self, group_name: str) -> torch.Tensor | dict[str, torch.Tenso
                 obs = term_cfg.noise.func(obs, term_cfg.noise)
             if term_cfg.clip:
                 obs = obs.clip_(min=term_cfg.clip[0], max=term_cfg.clip[1])
-            if term_cfg.scale:
+            if term_cfg.scale is not None:
                 obs = obs.mul_(term_cfg.scale)
             # add value to list
             group_obs[name] = obs
@@ -337,6 +337,23 @@ def _prepare_terms(self):
                 obs_dims = tuple(term_cfg.func(self._env, **term_cfg.params).shape)
                 self._group_obs_term_dim[group_name].append(obs_dims[1:])
 
+                # if scale is set, check if single float or tuple
+                if term_cfg.scale is not None:
+                    if not isinstance(term_cfg.scale, (float, int, tuple)):
+                        raise TypeError(
+                            f"Scale for observation term '{term_name}' in group '{group_name}'"
+                            f" is not of type float, int or tuple. Received: '{type(term_cfg.scale)}'."
+                        )
+                    if isinstance(term_cfg.scale, tuple) and len(term_cfg.scale) != obs_dims[1]:
+                        raise ValueError(
+                            f"Scale for observation term '{term_name}' in group '{group_name}'"
+                            f" does not match the dimensions of the observation. Expected: {obs_dims[1] - 1}"
+                            f" but received: {len(term_cfg.scale)}."
+                        )
+
+                    if isinstance(term_cfg.scale, tuple):
+                        term_cfg.scale = torch.tensor(term_cfg.scale, device=self._env.device)
+
                 # prepare modifiers for each observation
                 if term_cfg.modifiers is not None:
                     # initialize list of modifiers for term