Fixes joint out of position limits terminations

[GiulioRomualdi]

Description

This PR fixes shape‑handling bugs in the termination helpers joint_pos_out_of_limit and joint_pos_out_of_manual_limit:

• Root cause – both functions reduced across joints with torch.any(dim=1) before slicing with asset_cfg.joint_ids, leaving a 1‑D tensor and triggering an IndexError: too many indices.

• Fix – construct a single violations mask, optionally slice joints, then reduce with torch.any(dim=1).
  This preserves correct shapes ([num_envs]) and works for any joint‑selection type (None, slice, list, or tensor).

• Additional improvements

  • Made asset_cfg immutable by avoiding in‑place modification of joint_ids.
  • Added docstring details and harmonised logic between both helpers.

No new dependencies were introduced.

Fixes #2441

Type of change

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

Screenshots

Not applicable – logic‑only change.

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

[Mayankm96]

Here the implementation is correct since it is only operating over subset of joints.

[Mayankm96]

Isn't it simpler to do it this way?

if asset_cfg.joint_ids is None:
   asset_cfg.joint_ids = slice(None)

limits = asset.data.soft_joint_pos_limits[:, asset_cfg.joint_ids]
out_of_upper_limits = torch.any(asset.data.joint_pos[:, asset_cfg.joint_ids] > limits[..., 1], dim=1)
out_of_lower_limits = torch.any(asset.data.joint_pos[:, asset_cfg.joint_ids] < limits[..., 0], dim=1)
return torch.logical_or(out_of_upper_limits, out_of_lower_limits)

[Mayankm96]

I don't see why we need to make a masking first. As long as any of the joints are out of their limits, the returned value should be True. If that's not the case, then could you please also share a repro for the issue?