Updates NoiseModelWithAdditiveBias to apply per-feature bias sampling

[ozhanozen]

Description

This PR updates the NoiseModelWithAdditiveBias to apply per-feature bias sampling.

Previously, the model sampled a single scalar bias per episode and applied it uniformly across all feature dimensions (i.e., axis 1 of a (num_env, feature_dim) tensor). This PR changes the behavior to instead sample a separate bias value for each feature dimension, making the model more suitable for structured inputs such as positions, velocities, or multi-DOF actions.

Notes

Structured inputs typically contain semantically distinct components, like [x, y, z] coordinates, where applying the same bias across all components introduces unrealistic, fully correlated noise. Independent per-dimension bias sampling leads to more realistic and robust policy training, especially for sim-to-real transfer.

I’ve replaced the previous behavior with this new default, as I believe the original implementation could be misleading and not well-suited for many practical scenarios. However, if desired, I can make both behaviors available via a configuration flag (e.g., per_feature_bias=True), to retain backward compatibility.

Fixes #2759.

Type of change

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)

Checklist

• I have run the pre-commit checks with ./isaaclab.sh --format
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• I have updated the changelog and the corresponding version in the extension's config/extension.toml file
• I have added my name to the CONTRIBUTORS.md or my name already exists there

[ooctipus]

Thank you for PR, this is a very nice feature to have.

One small question as I review the code, if you relie on self.reset() to re-sample that expanded bias in-place, does that mean multi-dim intput will have a different bias value every step? My intuition tells me the bias per feature should at least stay consistent throughout the episode and reset at next episode, If I am not wrong, the code is currently resample-per-episode for 1D bias, yet resample-per-step for N-D bias.

[ozhanozen]

Thank you for PR, this is a very nice feature to have.

One small question as I review the code, if you relie on self.reset() to re-sample that expanded bias in-place, does that mean multi-dim intput will have a different bias value every step? My intuition tells me the bias per feature should at least stay consistent throughout the episode and reset at next episode, If I am not wrong, the code is currently resample-per-episode for 1D bias, yet resample-per-step for N-D bias.

Hi @ooctipus,

The code section I wrote:

if self._feature_dim is None:
   *_, self._feature_dim = data.shape
   self._bias = self._bias.repeat(1, self._feature_dim)
   self.reset()

only ever happens once, on the very first apply. At that moment:
1. self._feature_dim goes from None → some integer.
2. We expand the bias from shape (N,1) → (N,D).
3. We invoke self.reset(), which re-samples that full (N,D) bias in place.

After that first call, self._feature_dim is no longer None, so we never enter that branch again. apply jumps straight to return super().apply(data) + self._bias. Subsequent steps are as they were before. No extra calls to reset() during an episode: no per-step re-sampling.

[ooctipus]

Ahhh, I see. Thanks for the clarification, I think the code looks good and clean! Good Job!

[kellyguo11]

I think having an option to enable the backwards compatible approach will still be useful. Do you mind rebasing the changes on top of the other PR for noise model changes and update the extension versions?

[ozhanozen]

I think having an option to enable the backwards compatible approach will still be useful. Do you mind rebasing the changes on top of the other PR for noise model changes and update the extension versions?

I have added the sample_bias_per_component flag (default True) to enable/disable backwards compatibility, updated the PR with the latest changes, and updated extension version & changelog. PR is ready to be merged from my side.