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Fixes infinite loop in repeated_objects_terrain: respawn only wrong object samples #1612

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Fixes infinite loop in repeated_objects_terrain: respawn only wrong object samples #1612

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98ef885
Respawn only wrong object samples in repeated_objects_terrain
nicolaloi Dec 30, 2024
3696b29
Merge branch 'main' into fix-spawn-repeated-objects
nicolaloi Jan 2, 2025
e0cd081
Merge branch 'main' into fix-spawn-repeated-objects
nicolaloi Jan 14, 2025
f4fca60
Merge branch 'main' into fix-spawn-repeated-objects
nicolaloi Jan 18, 2025
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2 changes: 1 addition & 1 deletion source/extensions/omni.isaac.lab/config/extension.toml
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
[package]

# Note: Semantic Versioning is used: https://semver.org/
version = "0.30.4"
version = "0.30.5"

# Description
title = "Isaac Lab framework for Robot Learning"
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9 changes: 9 additions & 0 deletions source/extensions/omni.isaac.lab/docs/CHANGELOG.rst
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@@ -1,6 +1,15 @@
Changelog
---------

0.30.5 (2025-01-14)
~~~~~~~~~~~~~~~~~~~

Fixed
^^^^^

* Fixed the respawn of only wrong object samples in :func:`repeated_objects_terrain` of :mod:`omni.isaac.lab.terrains.trimesh` module. Previously, the function was respawning all objects in the scene instead of only the wrong object samples, which in worst case could lead to infinite respawn loop.


0.30.4 (2025-01-08)
~~~~~~~~~~~~~~~~~~~

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21 changes: 11 additions & 10 deletions source/extensions/omni.isaac.lab/omni/isaac/lab/terrains/trimesh/mesh_terrains.py
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Expand Up @@ -816,21 +816,22 @@ def repeated_objects_terrain(
platform_corners[0, :] *= 1 - platform_clearance
platform_corners[1, :] *= 1 + platform_clearance
# sample center for objects
while True:
object_centers = np.zeros((num_objects, 3))
object_centers[:, 0] = np.random.uniform(0, cfg.size[0], num_objects)
object_centers[:, 1] = np.random.uniform(0, cfg.size[1], num_objects)
object_centers = np.zeros((num_objects, 3))
mask_objects_left = np.ones((num_objects,), dtype=bool)
while np.any(mask_objects_left):
num_objects_left = mask_objects_left.sum()
object_centers[mask_objects_left, 0] = np.random.uniform(0, cfg.size[0], num_objects_left)
object_centers[mask_objects_left, 1] = np.random.uniform(0, cfg.size[1], num_objects_left)
# filter out the centers that are on the platform
is_within_platform_x = np.logical_and(
object_centers[:, 0] >= platform_corners[0, 0], object_centers[:, 0] <= platform_corners[1, 0]
object_centers[mask_objects_left, 0] >= platform_corners[0, 0],
object_centers[mask_objects_left, 0] <= platform_corners[1, 0],
)
is_within_platform_y = np.logical_and(
object_centers[:, 1] >= platform_corners[0, 1], object_centers[:, 1] <= platform_corners[1, 1]
object_centers[mask_objects_left, 1] >= platform_corners[0, 1],
object_centers[mask_objects_left, 1] <= platform_corners[1, 1],
)
masks = np.logical_and(is_within_platform_x, is_within_platform_y)
# if there are no objects on the platform, break
if not np.any(masks):
break
mask_objects_left[mask_objects_left] = np.logical_and(is_within_platform_x, is_within_platform_y)
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Thanks for the enhancement. Could you please add some comments on the logic of the loop and how mask_objects_left acts as the mask here?


# generate obstacles (but keep platform clean)
for index in range(len(object_centers)):
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