Fixes isaaclab.scene.reset_to to properly accept None as valid argument (#2970)

As reported by issue, isaaclab.scene.reset_to currently does not run
well with env_id = None, even though None is one of its default values.

This PR make sure it supports and and added tests that tensor input and
None should both work well

Fixes #2878

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- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
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- [ ] I have made corresponding changes to the documentation
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- [x] I have added tests that prove my fix is effective or that my
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source/isaaclab/config/extension.toml

 [package]
 
 # Note: Semantic Versioning is used: https://semver.org/
-version = "0.44.5"
+version = "0.44.6"
 
 # Description
 title = "Isaac Lab framework for Robot Learning"

source/isaaclab/docs/CHANGELOG.rst

 Changelog
 ---------
 
-0.44.5 (2025-07-28)
+0.44.6 (2025-07-28)
 ~~~~~~~~~~~~~~~~~~~
 
 Changed
@@ -9,7 +9,18 @@ Changed
 
 * Tweak default behavior for rendering preset modes.
 
-0.44.4 (2025-07-18)
+
+0.44.5 (2025-07-28)
+~~~~~~~~~~~~~~~~~~~
+
+Fixed
+^^^^^
+
+* Fixed :meth:`isaaclab.scene.reset_to` to properly accept None as valid argument.
+* Added tests to verify that argument types.
+
+
+0.44.4 (2025-07-22)
 ~~~~~~~~~~~~~~~~~~~
 
 Added
@@ -91,7 +102,7 @@ Added
 Added
 ^^^^^
 
-* Added :attr:`omni.isaac.lab.sensors.ContactSensorData.force_matrix_w_history` that tracks the history of the filtered contact forces in the world frame.
+* Added :attr:`~isaaclab.sensors.ContactSensorData.force_matrix_w_history` that tracks the history of the filtered contact forces in the world frame.
 
 
 0.42.24 (2025-06-25)

source/isaaclab/isaaclab/scene/interactive_scene.py

@@ -138,7 +138,8 @@ class InteractiveScene:
         self.env_prim_paths = self.cloner.generate_paths(f"{self.env_ns}/env", self.cfg.num_envs)
         # create source prim
         self.stage.DefinePrim(self.env_prim_paths[0], "Xform")
-
+        # allocate env indices
+        self._ALL_INDICES = torch.arange(self.cfg.num_envs, dtype=torch.long, device=self.device)
         # when replicate_physics=False, we assume heterogeneous environments and clone the xforms first.
         # this triggers per-object level cloning in the spawner.
         if not self.cfg.replicate_physics:
@@ -515,7 +516,7 @@ class InteractiveScene:
         """
         # resolve env_ids
         if env_ids is None:
-            env_ids = slice(None)
+            env_ids = self._ALL_INDICES
         # articulations
         for asset_name, articulation in self._articulations.items():
             asset_state = state["articulation"][asset_name]

source/isaaclab/test/scene/test_interactive_scene.py

@@ -12,6 +12,8 @@ simulation_app = AppLauncher(headless=True).app
 
 """Rest everything follows."""
 
+import torch
+
 import pytest
 
 import isaaclab.sim as sim_utils
@@ -20,7 +22,6 @@ from isaaclab.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg
 from isaaclab.scene import InteractiveScene, InteractiveSceneCfg
 from isaaclab.sensors import ContactSensorCfg
 from isaaclab.sim import build_simulation_context
-from isaaclab.terrains import TerrainImporterCfg
 from isaaclab.utils import configclass
 from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR
 
@@ -29,15 +30,9 @@ from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR
 class MySceneCfg(InteractiveSceneCfg):
     """Example scene configuration."""
 
-    # terrain - flat terrain plane
-    terrain = TerrainImporterCfg(
-        prim_path="/World/ground",
-        terrain_type="plane",
-    )
-
     # articulation
     robot = ArticulationCfg(
-        prim_path="/World/Robot",
+        prim_path="/World/envs/env_.*/Robot",
         spawn=sim_utils.UsdFileCfg(
             usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/IsaacSim/SimpleArticulation/revolute_articulation.usd"
         ),
@@ -47,7 +42,7 @@ class MySceneCfg(InteractiveSceneCfg):
     )
     # rigid object
     rigid_obj = RigidObjectCfg(
-        prim_path="/World/RigidObj",
+        prim_path="/World/envs/env_.*/RigidObj",
         spawn=sim_utils.CuboidCfg(
             size=(0.5, 0.5, 0.5),
             rigid_props=sim_utils.RigidBodyPropertiesCfg(
@@ -59,23 +54,23 @@ class MySceneCfg(InteractiveSceneCfg):
         ),
     )
 
-    # sensor
-    sensor = ContactSensorCfg(
-        prim_path="/World/Robot",
-    )
-    # extras - light
-    light = AssetBaseCfg(
-        prim_path="/World/light",
-        spawn=sim_utils.DistantLightCfg(),
-    )
 
+@pytest.fixture
+def setup_scene(request):
+    """Create simulation context with the specified device."""
+    device = request.getfixturevalue("device")
+    with build_simulation_context(device=device, auto_add_lighting=True, add_ground_plane=True) as sim:
+        sim._app_control_on_stop_handle = None
+
+        def make_scene(num_envs: int, env_spacing: float = 1.0):
+            scene_cfg = MySceneCfg(num_envs=num_envs, env_spacing=env_spacing)
+            return scene_cfg
 
-@pytest.fixture(scope="module")
-def setup_scene():
-    """Fixture to set up scene parameters."""
-    sim_dt = 0.001
-    scene_cfg = MySceneCfg(num_envs=1, env_spacing=1)
-    return sim_dt, scene_cfg
+        yield make_scene, sim
+    sim.stop()
+    sim.clear()
+    sim.clear_all_callbacks()
+    sim.clear_instance()
 
 
 @pytest.mark.parametrize("device", ["cuda:0", "cpu"])
@@ -86,11 +81,24 @@ def test_scene_entity_isolation(device, setup_scene):
     The scene at index 0 of the list will have all of its entities cleared manually, and
     the test compares that the data held in the scene at index 1 remained intact.
     """
-    sim_dt, scene_cfg = setup_scene
+    make_scene, sim = setup_scene
+    scene_cfg = make_scene(num_envs=1)
+    # set additional light to test 'extras' attribute of the scene
+    setattr(
+        scene_cfg,
+        "light",
+        AssetBaseCfg(
+            prim_path="/World/light",
+            spawn=sim_utils.DistantLightCfg(),
+        ),
+    )
+    # set additional sensor to test 'sensors' attribute of the scene
+    setattr(scene_cfg, "sensor", ContactSensorCfg(prim_path="/World/envs/env_.*/Robot"))
+
     scene_list = []
     # create two InteractiveScene instances
     for _ in range(2):
-        with build_simulation_context(device=device, dt=sim_dt) as _:
+        with build_simulation_context(device=device, dt=sim.get_physics_dt()) as _:
             scene = InteractiveScene(scene_cfg)
             scene_list.append(scene)
     scene_0 = scene_list[0]
@@ -109,3 +117,101 @@ def test_scene_entity_isolation(device, setup_scene):
     assert scene_0.sensors != scene_1.sensors
     assert scene_0.extras == dict()
     assert scene_0.extras != scene_1.extras
+
+
+@pytest.mark.parametrize("device", ["cuda:0", "cpu"])
+def test_relative_flag(device, setup_scene):
+    make_scene, sim = setup_scene
+    scene_cfg = make_scene(num_envs=4)
+    scene = InteractiveScene(scene_cfg)
+    sim.reset()
+
+    # test relative == False produces different result than relative == True
+    assert_state_different(scene.get_state(is_relative=False), scene.get_state(is_relative=True))
+
+    # test is relative == False
+    prev_state = scene.get_state(is_relative=False)
+    scene["robot"].write_joint_state_to_sim(
+        position=torch.rand_like(scene["robot"].data.joint_pos), velocity=torch.rand_like(scene["robot"].data.joint_pos)
+    )
+    next_state = scene.get_state(is_relative=False)
+    assert_state_different(prev_state, next_state)
+    scene.reset_to(prev_state, is_relative=False)
+    assert_state_equal(prev_state, scene.get_state(is_relative=False))
+
+    # test is relative == True
+    prev_state = scene.get_state(is_relative=True)
+    scene["robot"].write_joint_state_to_sim(
+        position=torch.rand_like(scene["robot"].data.joint_pos), velocity=torch.rand_like(scene["robot"].data.joint_pos)
+    )
+    next_state = scene.get_state(is_relative=True)
+    assert_state_different(prev_state, next_state)
+    scene.reset_to(prev_state, is_relative=True)
+    assert_state_equal(prev_state, scene.get_state(is_relative=True))
+
+
+@pytest.mark.parametrize("device", ["cuda:0", "cpu"])
+def test_reset_to_env_ids_input_types(device, setup_scene):
+    make_scene, sim = setup_scene
+    scene_cfg = make_scene(num_envs=4)
+    scene = InteractiveScene(scene_cfg)
+    sim.reset()
+
+    # test env_ids = None
+    prev_state = scene.get_state()
+    scene["robot"].write_joint_state_to_sim(
+        position=torch.rand_like(scene["robot"].data.joint_pos), velocity=torch.rand_like(scene["robot"].data.joint_pos)
+    )
+    scene.reset_to(prev_state, env_ids=None)
+    assert_state_equal(prev_state, scene.get_state())
+
+    # test env_ids = torch tensor
+    scene["robot"].write_joint_state_to_sim(
+        position=torch.rand_like(scene["robot"].data.joint_pos), velocity=torch.rand_like(scene["robot"].data.joint_pos)
+    )
+    scene.reset_to(prev_state, env_ids=torch.arange(scene.num_envs, device=scene.device))
+    assert_state_equal(prev_state, scene.get_state())
+
+
+def assert_state_equal(s1: dict, s2: dict, path=""):
+    """
+    Recursively assert that s1 and s2 have the same nested keys
+    and that every tensor leaf is exactly equal.
+    """
+    assert set(s1.keys()) == set(s2.keys()), f"Key mismatch at {path}: {s1.keys()} vs {s2.keys()}"
+    for k in s1:
+        v1, v2 = s1[k], s2[k]
+        subpath = f"{path}.{k}" if path else k
+        if isinstance(v1, dict):
+            assert isinstance(v2, dict), f"Type mismatch at {subpath}"
+            assert_state_equal(v1, v2, path=subpath)
+        else:
+            # leaf: should be a torch.Tensor
+            assert isinstance(v1, torch.Tensor) and isinstance(v2, torch.Tensor), f"Expected tensors at {subpath}"
+            if not torch.equal(v1, v2):
+                diff = (v1 - v2).abs().max()
+                pytest.fail(f"Tensor mismatch at {subpath}, max abs diff = {diff}")
+
+
+def assert_state_different(s1: dict, s2: dict, path=""):
+    """
+    Recursively scan s1 and s2 (which must have identical keys) and
+    succeed as soon as you find one tensor leaf that differs.
+    If you reach the end with everything equal, fail the test.
+    """
+    assert set(s1.keys()) == set(s2.keys()), f"Key mismatch at {path}: {s1.keys()} vs {s2.keys()}"
+    for k in s1:
+        v1, v2 = s1[k], s2[k]
+        subpath = f"{path}.{k}" if path else k
+        if isinstance(v1, dict):
+            # recurse; if any nested call returns (i.e. finds a diff), we propagate success
+            try:
+                assert_state_different(v1, v2, path=subpath)
+                return
+            except AssertionError:
+                continue
+        else:
+            assert isinstance(v1, torch.Tensor) and isinstance(v2, torch.Tensor), f"Expected tensors at {subpath}"
+            if not torch.equal(v1, v2):
+                return  # found a difference → success
+    pytest.fail(f"No differing tensor found in nested state at {path}")