Fixes IndexError in reset_joints_by_scale and reset_joints_by_offset

[Creampelt]

Description

Fixes the IndexError caused by simultaneously indexing env_ids and joint_ids in reset_joints_by_scale and reset_joints_by_offset.

Fixes # 2948

Type of change

• Bug fix (non-breaking change which fixes an issue)

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]

@Creampelt Sorry I was my miss!

Thank you for the fix, it looks very good XD

[jhs3330395]

This content has not yet been merged into the main branch.. (2025.07.24)

[jhs3330395]

Am I using git incorrectly? Still after downloading to git clone, the main branch does not have that issue updated, please check.
now main branch`s commit is this
083ca3c (HEAD -> main, origin/main, origin/HEAD) Shifts order of docker deployment documentation (#2984)

i can`t find this commit or branch in gitlab original code

[Creampelt]

@jhs3330395 this PR hasn't been merged yet -- I don't have push access, so it needs to be approved & merged by someone who does. I don't think you'll be able to see the commit in gitlab, since it is on a branch on my own fork of the repo.

@Mayankm96 let me know if there are any other edits you'd like to see; otherwise, feel free to merge whenever :)

[kellyguo11]

I can't seem to get CI to run properly :( but locally most tests are passing. @Creampelt @ooctipus have either of you tried running the unit tests locally just to be sure?

[ooctipus]

let me do that overnight :))

[Mayankm96]

I went over my own implementation for the fix. Sharing it here in case we find this implementation better:

def reset_joints_by_scale(
    env: ManagerBasedEnv,
    env_ids: torch.Tensor,
    position_range: tuple[float, float],
    velocity_range: tuple[float, float],
    asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
):
    """Reset the robot joints by scaling the default position and velocity by the given ranges.

    This function samples random values from the given ranges and scales the default joint positions and velocities
    by these values. The scaled values are then set into the physics simulation.
    """
    # extract the used quantities (to enable type-hinting)
    asset: Articulation = env.scene[asset_cfg.name]

    # cast env_ids to allow broadcasting
    if asset_cfg.joint_ids != slice(None):
        iter_env_ids = env_ids[:, None]
    else:
        iter_env_ids = env_ids

    # get default joint state
    joint_pos = asset.data.default_joint_pos[iter_env_ids, asset_cfg.joint_ids].clone()
    joint_vel = asset.data.default_joint_vel[iter_env_ids, asset_cfg.joint_ids].clone()

    # scale these values randomly
    joint_pos *= math_utils.sample_uniform(*position_range, joint_pos.shape, joint_pos.device)
    joint_vel *= math_utils.sample_uniform(*velocity_range, joint_vel.shape, joint_vel.device)

    # clamp joint pos to limits
    joint_pos_limits = asset.data.soft_joint_pos_limits[iter_env_ids, asset_cfg.joint_ids]
    joint_pos = joint_pos.clamp_(joint_pos_limits[..., 0], joint_pos_limits[..., 1])
    # clamp joint vel to limits
    joint_vel_limits = asset.data.soft_joint_vel_limits[iter_env_ids, asset_cfg.joint_ids]
    joint_vel = joint_vel.clamp_(-joint_vel_limits, joint_vel_limits)

    # set into the physics simulation
    asset.write_joint_state_to_sim(joint_pos, joint_vel, joint_ids=asset_cfg.joint_ids, env_ids=env_ids)

def reset_joints_by_offset(
    env: ManagerBasedEnv,
    env_ids: torch.Tensor,
    position_range: tuple[float, float],
    velocity_range: tuple[float, float],
    asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
):
    """Reset the robot joints with offsets around the default position and velocity by the given ranges.

    This function samples random values from the given ranges and biases the default joint positions and velocities
    by these values. The biased values are then set into the physics simulation.
    """
    # extract the used quantities (to enable type-hinting)
    asset: Articulation = env.scene[asset_cfg.name]

    # cast env_ids to allow broadcasting
    if asset_cfg.joint_ids != slice(None):
        iter_env_ids = env_ids[:, None]
    else:
        iter_env_ids = env_ids

    # get default joint state
    joint_pos = asset.data.default_joint_pos[iter_env_ids, asset_cfg.joint_ids].clone()
    joint_vel = asset.data.default_joint_vel[iter_env_ids, asset_cfg.joint_ids].clone()

    # bias these values randomly
    joint_pos += math_utils.sample_uniform(*position_range, joint_pos.shape, joint_pos.device)
    joint_vel += math_utils.sample_uniform(*velocity_range, joint_vel.shape, joint_vel.device)

    # clamp joint pos to limits
    joint_pos_limits = asset.data.soft_joint_pos_limits[iter_env_ids, asset_cfg.joint_ids]
    joint_pos = joint_pos.clamp_(joint_pos_limits[..., 0], joint_pos_limits[..., 1])
    # clamp joint vel to limits
    joint_vel_limits = asset.data.soft_joint_vel_limits[iter_env_ids, asset_cfg.joint_ids]
    joint_vel = joint_vel.clamp_(-joint_vel_limits, joint_vel_limits)

    # set into the physics simulation
    asset.write_joint_state_to_sim(joint_pos, joint_vel, joint_ids=asset_cfg.joint_ids, env_ids=env_ids)

[ooctipus]

Yeah I like it, thanks @Mayankm96
this could squeeze more performance if asset_cfg.joint_ids happens to be slice()
@Creampelt can you verify if that works

[Mayankm96]

@ooctipus @Creampelt Could you please take a look at above so we can merge the fix soon? :)

[Creampelt]

@Mayankm96 pushed and tested the edits, everything is good to go!

[ooctipus]

taking look at this right now! one ci passed I will make sure merge it soon