Updates NoiseModelWithAdditiveBias to apply per-feature bias sampling

source/isaaclab/config/extension.toml

@@ -1,7 +1,7 @@
 [package]
 
 # Note: Semantic Versioning is used: https://semver.org/
-version = "0.40.6"
+version = "0.40.X"
 
 # Description
 title = "Isaac Lab framework for Robot Learning"

source/isaaclab/docs/CHANGELOG.rst

@@ -1,6 +1,16 @@
 Changelog
 ---------
 
+## [Unreleased]
+~~~~~~~~~~~~~~~
+
+Changed
+^^^^^^^
+
+* Changed :class:`~isaaclab.utils.noise.noise_model.NoiseModelWithAdditiveBias` to sample each bias feature dimension independently.
+  Previously, for multi-dimensional data, e.g., a 3D position vector, all feature dimensions shared the same bias value.
+
+
 0.40.6 (2025-06-12)
 ~~~~~~~~~~~~~~~~~~~
 

source/isaaclab/isaaclab/utils/noise/noise_model.py

@@ -154,6 +154,7 @@ def __init__(self, noise_model_cfg: noise_cfg.NoiseModelWithAdditiveBiasCfg, num
         # store the bias noise configuration
         self._bias_noise_cfg = noise_model_cfg.bias_noise_cfg
         self._bias = torch.zeros((num_envs, 1), device=self._device)
+        self._feature_dim: int | None = None
 
     def reset(self, env_ids: Sequence[int] | None = None):
         """Reset the noise model.
@@ -179,4 +180,11 @@ def apply(self, data: torch.Tensor) -> torch.Tensor:
         Returns:
             The data with the noise applied. Shape is the same as the input data.
         """
+        # on first apply, expand bias to match last dim of data
+        if self._feature_dim is None:
+            *_, self._feature_dim = data.shape
+            # expand bias from (num_envs,1) to (num_envs, feature_dim)
+            self._bias = self._bias.repeat(1, self._feature_dim)
+            # now re-sample that expanded bias in-place
+            self.reset()
         return super().apply(data) + self._bias