Fixes setting of root velocities in the event term `reset_root_state_from_terrain` (#1884)

# Description

When reset_root_state_from_terrain is called from an EventTerm, we
cannot predict which env_ids will be targeted. To prevent program
crashes due to tensor size mismatches, this MR modifies the velocity
calculation to use only the specified env_ids instead of referencing all
environments.

Changes:
- Updated velocity calculation to use env_ids indexing for
default_root_state
- This ensures tensor dimensions match between default_root_state and
rand_samples

Fixes #1882 

## Type of change

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

## Checklist

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

CONTRIBUTORS.md

@@ -86,6 +86,7 @@ Guidelines for modifications:
 * Rosario Scalise
 * Ryley McCarroll
 * Shafeef Omar
+* Shundo Kishi
 * Stephan Pleines
 * Vladimir Fokow
 * Wei Yang

source/isaaclab/docs/CHANGELOG.rst

 Changelog
 ---------
 
+0.34.2 (2025-02-21)
+~~~~~~~~~~~~~~~~~~~~
+
+Fixed
+^^^^^
+
+* Fixed setting of root velocities inside the event term :meth:`reset_root_state_from_terrain`. Earlier, the indexing
+  based on the environment IDs was missing.
+
+
 0.34.1 (2025-02-17)
 ~~~~~~~~~~~~~~~~~~~
 

source/isaaclab/isaaclab/envs/mdp/events.py

@@ -792,7 +792,7 @@ def reset_root_state_from_terrain(
     ranges = torch.tensor(range_list, device=asset.device)
     rand_samples = math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], (len(env_ids), 6), device=asset.device)
 
-    velocities = asset.data.default_root_state[:, 7:13] + rand_samples
+    velocities = asset.data.default_root_state[env_ids, 7:13] + rand_samples
 
     # set into the physics simulation
     asset.write_root_pose_to_sim(torch.cat([positions, orientations], dim=-1), env_ids=env_ids)