Fixes orientation resampling logic in reset_root_state_uniform event

CONTRIBUTORS.md

@@ -51,6 +51,7 @@ Guidelines for modifications:
 * Rosario Scalise
 * Shafeef Omar
 * Vladimir Fokow
+* Zhengyu Zhang
 
 ## Acknowledgements
 

source/extensions/omni.isaac.lab/config/extension.toml

@@ -1,7 +1,7 @@
 [package]
 
 # Note: Semantic Versioning is used: https://semver.org/
-version = "0.17.12"
+version = "0.17.13"
 
 # Description
 title = "Isaac Lab framework for Robot Learning"

source/extensions/omni.isaac.lab/docs/CHANGELOG.rst

@@ -1,6 +1,16 @@
 Changelog
 ---------
 
+0.17.13 (2024-06-13)
+~~~~~~~~~~~~~~~~~~~~
+
+Fixed
+^^^^^
+
+* Fixed the orientation reset logic in :func:`omni.isaac.lab.envs.mdp.events.reset_root_state_uniform` to make it relative to the default orientation.
+  Earlier, the position was sampled relative to the default and the orientation not.
+
+
 0.17.12 (2024-06-13)
 ~~~~~~~~~~~~~~~~~~~~
 

source/extensions/omni.isaac.lab/omni/isaac/lab/assets/articulation/articulation.py

@@ -230,7 +230,7 @@ def find_fixed_tendons(
     ) -> tuple[list[int], list[str]]:
         """Find fixed tendons in the articulation based on the name keys.
 
-        Please see the :func:`omni.isaac.orbit.utils.string.resolve_matching_names` function for more information
+        Please see the :func:`omni.isaac.lab.utils.string.resolve_matching_names` function for more information
         on the name matching.
 
         Args:

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/mdp/events.py

@@ -611,8 +611,8 @@ def reset_root_state_uniform(
     rand_samples = math_utils.sample_uniform(ranges[:, 0], ranges[:, 1], (len(env_ids), 6), device=asset.device)
 
     positions = root_states[:, 0:3] + env.scene.env_origins[env_ids] + rand_samples[:, 0:3]
-    orientations = math_utils.quat_from_euler_xyz(rand_samples[:, 3], rand_samples[:, 4], rand_samples[:, 5])
-
+    orientations_delta = math_utils.quat_from_euler_xyz(rand_samples[:, 3], rand_samples[:, 4], rand_samples[:, 5])
+    orientations = math_utils.quat_mul(root_states[:, 3:7], orientations_delta)
     # velocities
     range_list = [velocity_range.get(key, (0.0, 0.0)) for key in ["x", "y", "z", "roll", "pitch", "yaw"]]
     ranges = torch.tensor(range_list, device=asset.device)