Merge branch 'main' into lgulich/ignore-articulation-when-loading-rig…

…id-body

Signed-off-by: lgulich <[email protected]>

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

@@ -1,7 +1,7 @@
 [package]
 
 # Note: Semantic Versioning is used: https://semver.org/
-version = "0.27.11"
+version = "0.27.13"
 
 # Description
 title = "Isaac Lab framework for Robot Learning"

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

@@ -1,17 +1,34 @@
 Changelog
 ---------
 
-0.27.12 (2024-11-04)
+0.27.14 (2024-11-04)
 ~~~~~~~~~~~~~~~~~~~~
 
-Added
-^^^^^
-
 * If a USD that contains an articulation root is loaded using a
   :attr:`omni.isaac_lab.assets.RigidBody` we now fail unless the articulation root is explicitly
   disabled.
 
 
+0.27.13 (2024-10-30)
+~~~~~~~~~~~~~~~~~~~~
+
+Added
+^^^^^
+
+* Added the attributes :attr:`~omni.isaac.lab.sim.converters.MeshConverterCfg.translation`, :attr:`~omni.isaac.lab.sim.converters.MeshConverterCfg.rotation`,
+  :attr:`~omni.isaac.lab.sim.converters.MeshConverterCfg.scale` to translate, rotate, and scale meshes
+  when importing them with :class:`~omni.isaac.lab.sim.converters.MeshConverter`.
+
+
+0.27.12 (2024-01-04)
+~~~~~~~~~~~~~~~~~~~
+
+Removed
+^^^^^^^
+
+* Removed TensorDict usage in favor of Python dictionary in sensors
+
+
 0.27.11 (2024-10-31)
 ~~~~~~~~~~~~~~~~~~~~
 

source/extensions/omni.isaac.lab/omni/isaac/lab/sensors/camera/camera.py

@@ -9,7 +9,6 @@
 import re
 import torch
 from collections.abc import Sequence
-from tensordict import TensorDict
 from typing import TYPE_CHECKING, Any, Literal
 
 import carb
@@ -156,7 +155,7 @@ def __str__(self) -> str:
         # message for class
         return (
             f"Camera @ '{self.cfg.prim_path}': \n"
-            f"\tdata types   : {self.data.output.sorted_keys} \n"
+            f"\tdata types   : {list(self.data.output.keys())} \n"
             f"\tsemantic filter : {self.cfg.semantic_filter}\n"
             f"\tcolorize semantic segm.   : {self.cfg.colorize_semantic_segmentation}\n"
             f"\tcolorize instance segm.   : {self.cfg.colorize_instance_segmentation}\n"
@@ -497,7 +496,7 @@ def _update_buffers_impl(self, env_ids: Sequence[int]):
         self._update_poses(env_ids)
         # -- read the data from annotator registry
         # check if buffer is called for the first time. If so then, allocate the memory
-        if len(self._data.output.sorted_keys) == 0:
+        if len(self._data.output) == 0:
             # this is the first time buffer is called
             # it allocates memory for all the sensors
             self._create_annotator_data()
@@ -552,7 +551,7 @@ def _create_buffers(self):
         # lazy allocation of data dictionary
         # since the size of the output data is not known in advance, we leave it as None
         # the memory will be allocated when the buffer() function is called for the first time.
-        self._data.output = TensorDict({}, batch_size=self._view.count, device=self.device)
+        self._data.output = {}
         self._data.info = [{name: None for name in self.cfg.data_types} for _ in range(self._view.count)]
 
     def _update_intrinsic_matrices(self, env_ids: Sequence[int]):

source/extensions/omni.isaac.lab/omni/isaac/lab/sensors/camera/camera_data.py

@@ -5,7 +5,6 @@
 
 import torch
 from dataclasses import dataclass
-from tensordict import TensorDict
 from typing import Any
 
 from omni.isaac.lab.utils.math import convert_camera_frame_orientation_convention
@@ -47,7 +46,7 @@ class CameraData:
     Shape is (N, 3, 3) where N is the number of sensors.
     """
 
-    output: TensorDict = None
+    output: dict[str, torch.Tensor] = None
     """The retrieved sensor data with sensor types as key.
 
     The format of the data is available in the `Replicator Documentation`_. For semantic-based data,

source/extensions/omni.isaac.lab/omni/isaac/lab/sensors/camera/tiled_camera.py

@@ -9,7 +9,6 @@
 import numpy as np
 import torch
 from collections.abc import Sequence
-from tensordict import TensorDict
 from typing import TYPE_CHECKING, Any
 
 import carb
@@ -106,7 +105,7 @@ def __str__(self) -> str:
         # message for class
         return (
             f"Tiled Camera @ '{self.cfg.prim_path}': \n"
-            f"\tdata types   : {self.data.output.sorted_keys} \n"
+            f"\tdata types   : {list(self.data.output.keys())} \n"
             f"\tsemantic filter : {self.cfg.semantic_filter}\n"
             f"\tcolorize semantic segm.   : {self.cfg.colorize_semantic_segmentation}\n"
             f"\tcolorize instance segm.   : {self.cfg.colorize_instance_segmentation}\n"
@@ -372,7 +371,7 @@ def _create_buffers(self):
                     (self._view.count, self.cfg.height, self.cfg.width, 1), device=self.device, dtype=torch.int32
                 ).contiguous()
 
-        self._data.output = TensorDict(data_dict, batch_size=self._view.count, device=self.device)
+        self._data.output = data_dict
         self._data.info = dict()
 
     def _tiled_image_shape(self) -> tuple[int, int]:

source/extensions/omni.isaac.lab/omni/isaac/lab/sensors/ray_caster/ray_caster_camera.py

@@ -7,7 +7,6 @@
 
 import torch
 from collections.abc import Sequence
-from tensordict import TensorDict
 from typing import TYPE_CHECKING, ClassVar, Literal
 
 import omni.isaac.core.utils.stage as stage_utils
@@ -347,7 +346,7 @@ def _create_buffers(self):
         self._data.image_shape = self.image_shape
         # -- output data
         # create the buffers to store the annotator data.
-        self._data.output = TensorDict({}, batch_size=self._view.count, device=self.device)
+        self._data.output = {}
         self._data.info = [{name: None for name in self.cfg.data_types}] * self._view.count
         for name in self.cfg.data_types:
             if name in ["distance_to_image_plane", "distance_to_camera"]:

source/extensions/omni.isaac.lab/omni/isaac/lab/sim/converters/asset_converter_base.py

@@ -54,6 +54,8 @@ def __init__(self, cfg: AssetConverterBaseCfg):
         Raises:
             ValueError: When provided asset file does not exist.
         """
+        # check that the config is valid
+        cfg.validate()
         # check if the asset file exists
         if not check_file_path(cfg.asset_path):
             raise ValueError(f"The asset path does not exist: {cfg.asset_path}")

source/extensions/omni.isaac.lab/omni/isaac/lab/sim/converters/mesh_converter.py

@@ -10,7 +10,7 @@
 import omni.kit.commands
 import omni.usd
 from omni.isaac.core.utils.extensions import enable_extension
-from pxr import Tf, Usd, UsdGeom, UsdPhysics, UsdUtils
+from pxr import Gf, Tf, Usd, UsdGeom, UsdPhysics, UsdUtils
 
 from omni.isaac.lab.sim.converters.asset_converter_base import AssetConverterBase
 from omni.isaac.lab.sim.converters.mesh_converter_cfg import MeshConverterCfg
@@ -64,12 +64,13 @@ def __init__(self, cfg: MeshConverterCfg):
     def _convert_asset(self, cfg: MeshConverterCfg):
         """Generate USD from OBJ, STL or FBX.
 
-        It stores the asset in the following format:
+        The USD file has Y-up axis and is scaled to meters.
+        The asset hierarchy is arranged as follows:
 
-        /file_name (default prim)
-          |- /geometry <- Made instanceable if requested
-            |- /Looks
-            |- /mesh
+        .. code-block:: none
+            mesh_file_basename (default prim)
+                |- /geometry/Looks
+                |- /geometry/mesh
 
         Args:
             cfg: The configuration for conversion of mesh to USD.
@@ -93,15 +94,25 @@ def _convert_asset(self, cfg: MeshConverterCfg):
 
         # Convert USD
         asyncio.get_event_loop().run_until_complete(
-            self._convert_mesh_to_usd(
-                in_file=cfg.asset_path, out_file=self.usd_path, prim_path=f"/{mesh_file_basename}"
-            )
+            self._convert_mesh_to_usd(in_file=cfg.asset_path, out_file=self.usd_path)
         )
+        # Create a new stage, set Z up and meters per unit
+        temp_stage = Usd.Stage.CreateInMemory()
+        UsdGeom.SetStageUpAxis(temp_stage, UsdGeom.Tokens.z)
+        UsdGeom.SetStageMetersPerUnit(temp_stage, 1.0)
+        UsdPhysics.SetStageKilogramsPerUnit(temp_stage, 1.0)
+        # Add mesh to stage
+        base_prim = temp_stage.DefinePrim(f"/{mesh_file_basename}", "Xform")
+        prim = temp_stage.DefinePrim(f"/{mesh_file_basename}/geometry", "Xform")
+        prim.GetReferences().AddReference(self.usd_path)
+        temp_stage.SetDefaultPrim(base_prim)
+        temp_stage.Export(self.usd_path)
+
         # Open converted USD stage
-        # note: This opens a new stage and does not use the stage created earlier by the user
-        # create a new stage
         stage = Usd.Stage.Open(self.usd_path)
-        # add USD to stage cache
+        # Need to reload the stage to get the new prim structure, otherwise it can be taken from the cache
+        stage.Reload()
+        # Add USD to stage cache
         stage_id = UsdUtils.StageCache.Get().Insert(stage)
         # Get the default prim (which is the root prim) -- "/{mesh_file_basename}"
         xform_prim = stage.GetDefaultPrim()
@@ -121,6 +132,32 @@ def _convert_asset(self, cfg: MeshConverterCfg):
                     )
         # Delete the old Xform and make the new Xform the default prim
         stage.SetDefaultPrim(xform_prim)
+        # Apply default Xform rotation to mesh -> enable to set rotation and scale
+        omni.kit.commands.execute(
+            "CreateDefaultXformOnPrimCommand",
+            prim_path=xform_prim.GetPath(),
+            **{"stage": stage},
+        )
+
+        # Apply translation, rotation, and scale to the Xform
+        geom_xform = UsdGeom.Xform(geom_prim)
+        geom_xform.ClearXformOpOrder()
+
+        # Remove any existing rotation attributes
+        rotate_attr = geom_prim.GetAttribute("xformOp:rotateXYZ")
+        if rotate_attr:
+            geom_prim.RemoveProperty(rotate_attr.GetName())
+
+        # translation
+        translate_op = geom_xform.AddTranslateOp(UsdGeom.XformOp.PrecisionDouble)
+        translate_op.Set(Gf.Vec3d(*cfg.translation))
+        # rotation
+        orient_op = geom_xform.AddOrientOp(UsdGeom.XformOp.PrecisionDouble)
+        orient_op.Set(Gf.Quatd(*cfg.rotation))
+        # scale
+        scale_op = geom_xform.AddScaleOp(UsdGeom.XformOp.PrecisionDouble)
+        scale_op.Set(Gf.Vec3d(*cfg.scale))
+
         # Handle instanceable
         # Create a new Xform prim that will be the prototype prim
         if cfg.make_instanceable:
@@ -158,40 +195,28 @@ def _convert_asset(self, cfg: MeshConverterCfg):
     """
 
     @staticmethod
-    async def _convert_mesh_to_usd(
-        in_file: str, out_file: str, prim_path: str = "/World", load_materials: bool = True
-    ) -> bool:
+    async def _convert_mesh_to_usd(in_file: str, out_file: str, load_materials: bool = True) -> bool:
         """Convert mesh from supported file types to USD.
 
         This function uses the Omniverse Asset Converter extension to convert a mesh file to USD.
         It is an asynchronous function and should be called using `asyncio.get_event_loop().run_until_complete()`.
 
         The converted asset is stored in the USD format in the specified output file.
-        The USD file has Y-up axis and is scaled to meters.
-
-        The asset hierarchy is arranged as follows:
-
-        .. code-block:: none
-            prim_path (default prim)
-                |- /geometry/Looks
-                |- /geometry/mesh
+        The USD file has Y-up axis and is scaled to cm.
 
         Args:
             in_file: The file to convert.
             out_file: The path to store the output file.
-            prim_path: The prim path of the mesh.
             load_materials: Set to True to enable attaching materials defined in the input file
                 to the generated USD mesh. Defaults to True.
 
         Returns:
             True if the conversion succeeds.
         """
         enable_extension("omni.kit.asset_converter")
-        enable_extension("omni.usd.metrics.assembler")
 
         import omni.kit.asset_converter
         import omni.usd
-        from omni.metrics.assembler.core import get_metrics_assembler_interface
 
         # Create converter context
         converter_context = omni.kit.asset_converter.AssetConverterContext()
@@ -212,29 +237,9 @@ async def _convert_mesh_to_usd(
 
         # Create converter task
         instance = omni.kit.asset_converter.get_instance()
-        out_file_non_metric = out_file.replace(".usd", "_non_metric.usd")
-        task = instance.create_converter_task(in_file, out_file_non_metric, None, converter_context)
+        task = instance.create_converter_task(in_file, out_file, None, converter_context)
         # Start conversion task and wait for it to finish
-        success = True
-        while True:
-            success = await task.wait_until_finished()
-            if not success:
-                await asyncio.sleep(0.1)
-            else:
-                break
-
-        temp_stage = Usd.Stage.CreateInMemory()
-        UsdGeom.SetStageUpAxis(temp_stage, UsdGeom.Tokens.z)
-        UsdGeom.SetStageMetersPerUnit(temp_stage, 1.0)
-        UsdPhysics.SetStageKilogramsPerUnit(temp_stage, 1.0)
-
-        base_prim = temp_stage.DefinePrim(prim_path, "Xform")
-        prim = temp_stage.DefinePrim(f"{prim_path}/geometry", "Xform")
-        prim.GetReferences().AddReference(out_file_non_metric)
-        cache = UsdUtils.StageCache.Get()
-        cache.Insert(temp_stage)
-        stage_id = cache.GetId(temp_stage).ToLongInt()
-        get_metrics_assembler_interface().resolve_stage(stage_id)
-        temp_stage.SetDefaultPrim(base_prim)
-        temp_stage.Export(out_file)
+        success = await task.wait_until_finished()
+        if not success:
+            raise RuntimeError(f"Failed to convert {in_file} to USD. Error: {task.get_error_message()}")
         return success

source/extensions/omni.isaac.lab/omni/isaac/lab/sim/converters/mesh_converter_cfg.py

@@ -12,21 +12,21 @@
 class MeshConverterCfg(AssetConverterBaseCfg):
     """The configuration class for MeshConverter."""
 
-    mass_props: schemas_cfg.MassPropertiesCfg = None
+    mass_props: schemas_cfg.MassPropertiesCfg | None = None
     """Mass properties to apply to the USD. Defaults to None.
 
     Note:
         If None, then no mass properties will be added.
     """
 
-    rigid_props: schemas_cfg.RigidBodyPropertiesCfg = None
+    rigid_props: schemas_cfg.RigidBodyPropertiesCfg | None = None
     """Rigid body properties to apply to the USD. Defaults to None.
 
     Note:
         If None, then no rigid body properties will be added.
     """
 
-    collision_props: schemas_cfg.CollisionPropertiesCfg = None
+    collision_props: schemas_cfg.CollisionPropertiesCfg | None = None
     """Collision properties to apply to the USD. Defaults to None.
 
     Note:
@@ -42,3 +42,12 @@ class MeshConverterCfg(AssetConverterBaseCfg):
 
     "none" causes no collision mesh to be added.
     """
+
+    translation: tuple[float, float, float] = (0.0, 0.0, 0.0)
+    """The translation of the mesh to the origin. Defaults to (0.0, 0.0, 0.0)."""
+
+    rotation: tuple[float, float, float, float] = (1.0, 0.0, 0.0, 0.0)
+    """The rotation of the mesh in quaternion format (w, x, y, z). Defaults to (1.0, 0.0, 0.0, 0.0)."""
+
+    scale: tuple[float, float, float] = (1.0, 1.0, 1.0)
+    """The scale of the mesh. Defaults to (1.0, 1.0, 1.0)."""

source/extensions/omni.isaac.lab/omni/isaac/lab/sim/converters/urdf_converter_cfg.py

@@ -30,7 +30,7 @@ class UrdfConverterCfg(AssetConverterBaseCfg):
     """Decompose a convex mesh into smaller pieces for a closer fit. Defaults to False."""
 
     fix_base: bool = MISSING
-    """Create a fix joint to the root/base link. Defaults to True."""
+    """Create a fix joint to the root/base link."""
 
     merge_fixed_joints: bool = False
     """Consolidate links that are connected by fixed joints. Defaults to False."""

source/extensions/omni.isaac.lab/test/sensors/test_camera.py

@@ -121,7 +121,7 @@ def test_camera_init(self):
             # update camera
             camera.update(self.dt)
             # check image data
-            for im_data in camera.data.output.to_dict().values():
+            for im_data in camera.data.output.values():
                 self.assertEqual(im_data.shape, (1, self.camera_cfg.height, self.camera_cfg.width, 1))
 
     def test_camera_init_offset(self):
@@ -228,7 +228,7 @@ def test_multi_camera_init(self):
             cam_2.update(self.dt)
             # check image data
             for cam in [cam_1, cam_2]:
-                for im_data in cam.data.output.to_dict().values():
+                for im_data in cam.data.output.values():
                     self.assertEqual(im_data.shape, (1, self.camera_cfg.height, self.camera_cfg.width, 1))
 
     def test_multi_camera_with_different_resolution(self):
@@ -705,7 +705,7 @@ def test_throughput(self):
             with Timer(f"Time taken for writing data with shape {camera.image_shape}   "):
                 # Pack data back into replicator format to save them using its writer
                 rep_output = {"annotators": {}}
-                camera_data = convert_dict_to_backend(camera.data.output[0].to_dict(), backend="numpy")
+                camera_data = convert_dict_to_backend({k: v[0] for k, v in camera.data.output.items()}, backend="numpy")
                 for key, data, info in zip(camera_data.keys(), camera_data.values(), camera.data.info[0].values()):
                     if info is not None:
                         rep_output["annotators"][key] = {"render_product": {"data": data, **info}}