Update FrameTransformer to handle external frames

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

@@ -3,8 +3,14 @@
 #
 # SPDX-License-Identifier: BSD-3-Clause
 
+# i Copyright (c) 2022-2024, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
 from __future__ import annotations
 
+import re
 import torch
 from collections.abc import Sequence
 from typing import TYPE_CHECKING
@@ -136,9 +142,9 @@ def _initialize_impl(self):
             self._source_frame_offset_pos = source_frame_offset_pos.unsqueeze(0).repeat(self._num_envs, 1)
             self._source_frame_offset_quat = source_frame_offset_quat.unsqueeze(0).repeat(self._num_envs, 1)
 
-        # Keep track of mapping from the rigid body name to the desired frame, as there may be multiple frames
+        # Keep track of mapping from the rigid body name to the desired frames and prim path, as there may be multiple frames
         # based upon the same body name and we don't want to create unnecessary views
-        body_names_to_frames: dict[str, set[str]] = {}
+        body_names_to_frames: dict[str, dict[str, set[str]]] = {}
         # The offsets associated with each target frame
         target_offsets: dict[str, dict[str, torch.Tensor]] = {}
         # The frames whose offsets are not identity
@@ -180,9 +186,10 @@ def _initialize_impl(self):
 
                 # Keep track of which frames are associated with which bodies
                 if body_name in body_names_to_frames:
-                    body_names_to_frames[body_name].add(frame_name)
+                    body_names_to_frames[body_name]["frames"].add(frame_name)
                 else:
-                    body_names_to_frames[body_name] = {frame_name}
+                    # Store the first matching prim path
+                    body_names_to_frames[body_name] = {"frames": {frame_name}, "prim_path": matching_prim_path}
 
                 if offset is not None:
                     offset_pos = torch.tensor(offset.pos, device=self.device)
@@ -206,29 +213,42 @@ def _initialize_impl(self):
             )
 
         # The names of bodies that RigidPrimView will be tracking to later extract transforms from
-        tracked_body_names = list(body_names_to_frames.keys())
-        # Construct regex expression for the body names
-        body_names_regex = r"(" + "|".join(tracked_body_names) + r")"
-        body_names_regex = f"{self.cfg.prim_path.rsplit('/', 1)[0]}/{body_names_regex}"
+        tracked_prim_paths = [body_names_to_frames[body_name]["prim_path"] for body_name in body_names_to_frames.keys()]
+        tracked_body_names = [body_name for body_name in body_names_to_frames.keys()]
+
+        body_names_regex = [tracked_prim_path.replace("env_0", "env_*") for tracked_prim_path in tracked_prim_paths]
+
         # Create simulation view
         self._physics_sim_view = physx.create_simulation_view(self._backend)
         self._physics_sim_view.set_subspace_roots("/")
         # Create a prim view for all frames and initialize it
         # order of transforms coming out of view will be source frame followed by target frame(s)
-        self._frame_physx_view = self._physics_sim_view.create_rigid_body_view(body_names_regex.replace(".*", "*"))
+        # self._frame_physx_view = self._physics_sim_view.create_rigid_body_view('/World/envs/env_*/{Robot/base,Robot/LH_SHANK,Robot/LF_SHANK,cube}')
+        self._frame_physx_view = self._physics_sim_view.create_rigid_body_view(body_names_regex)
 
         # Determine the order in which regex evaluated body names so we can later index into frame transforms
         # by frame name correctly
         all_prim_paths = self._frame_physx_view.prim_paths
 
+        def extract_env_num(item):
+            match = re.search(r"env_(\d+)(.*)", item)
+            return (int(match.group(1)), match.group(2))
+
+        # Find the indices that would reorganize output to be per environment. We want `env_1/blah` to come before `env_11/blah`
+        # so we need to use a custom key function
+        sorted_indexed_prim_paths = sorted(list(enumerate(all_prim_paths)), key=lambda x: extract_env_num(x[1]))
+        self._per_env_indices = [index for index, _ in sorted_indexed_prim_paths]
+        sorted_prim_paths = [all_prim_paths[i] for i in self._per_env_indices]
+
         # Only need first env as the names and their ordering are the same across environments
-        first_env_prim_paths = all_prim_paths[0 : len(tracked_body_names)]
+        first_env_prim_paths = [prim_path for prim_path in sorted_prim_paths if "env_0" in prim_path]
         first_env_body_names = [first_env_prim_path.split("/")[-1] for first_env_prim_path in first_env_prim_paths]
 
         # Re-parse the list as it may have moved when resolving regex above
         # -- source frame
         self._source_frame_body_name = self.cfg.prim_path.split("/")[-1]
         source_frame_index = first_env_body_names.index(self._source_frame_body_name)
+
         # -- target frames
         self._target_frame_body_names = first_env_body_names[:]
         self._target_frame_body_names.remove(self._source_frame_body_name)
@@ -248,11 +268,15 @@ def _initialize_impl(self):
         # when updating sensor in _update_buffers_impl
         duplicate_frame_indices = []
 
+        # The position and rotation components of target frame offsets
+        self._target_frame_offset_pos = torch.zeros(0, 3, device=self.device)
+        self._target_frame_offset_quat = torch.zeros(0, 4, device=self.device)
+
         # Go through each body name and determine the number of duplicates we need for that frame
         # and extract the offsets. This is all done to handles the case where multiple frames
         # reference the same body, but have different names and/or offsets
         for i, body_name in enumerate(self._target_frame_body_names):
-            for frame in body_names_to_frames[body_name]:
+            for frame in body_names_to_frames[body_name]["frames"]:
                 target_frame_offset_pos.append(target_offsets[frame]["pos"])
                 target_frame_offset_quat.append(target_offsets[frame]["quat"])
                 self._target_frame_names.append(frame)
@@ -288,6 +312,10 @@ def _update_buffers_impl(self, env_ids: Sequence[int]):
         # Extract transforms from view - shape is:
         # (the total number of source and target body frames being tracked * self._num_envs, 7)
         transforms = self._frame_physx_view.get_transforms()
+
+        # Reorder the transforms to be per environment as is expected of SensorData
+        transforms = transforms[self._per_env_indices]
+
         # Convert quaternions as PhysX uses xyzw form
         transforms[:, 3:] = convert_quat(transforms[:, 3:], to="wxyz")
 

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

@@ -26,6 +26,7 @@
 
 import omni.isaac.lab.sim as sim_utils
 import omni.isaac.lab.utils.math as math_utils
+from omni.isaac.lab.assets import RigidObjectCfg
 from omni.isaac.lab.scene import InteractiveScene, InteractiveSceneCfg
 from omni.isaac.lab.sensors import FrameTransformerCfg, OffsetCfg
 from omni.isaac.lab.terrains import TerrainImporterCfg
@@ -62,6 +63,19 @@ class MySceneCfg(InteractiveSceneCfg):
     # sensors - frame transformer (filled inside unit test)
     frame_transformer: FrameTransformerCfg = None
 
+    # block
+    cube: RigidObjectCfg = RigidObjectCfg(
+        prim_path="{ENV_REGEX_NS}/cube",
+        spawn=sim_utils.CuboidCfg(
+            size=(0.2, 0.2, 0.2),
+            rigid_props=sim_utils.RigidBodyPropertiesCfg(max_depenetration_velocity=1.0),
+            mass_props=sim_utils.MassPropertiesCfg(mass=1.0),
+            physics_material=sim_utils.RigidBodyMaterialCfg(),
+            visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.5, 0.0, 0.0)),
+        ),
+        init_state=RigidObjectCfg.InitialStateCfg(pos=(2.0, 0.0, 5)),
+    )
+
 
 class TestFrameTransformer(unittest.TestCase):
     """Test for frame transformer sensor."""
@@ -71,7 +85,7 @@ def setUp(self):
         # Create a new stage
         stage_utils.create_new_stage()
         # Load kit helper
-        self.sim = sim_utils.SimulationContext(sim_utils.SimulationCfg(dt=0.005))
+        self.sim = sim_utils.SimulationContext(sim_utils.SimulationCfg(dt=0.005, device="cpu"))
         # Set main camera
         self.sim.set_camera_view(eye=[5, 5, 5], target=[0.0, 0.0, 0.0])
 
@@ -90,8 +104,7 @@ def tearDown(self):
     def test_frame_transformer_feet_wrt_base(self):
         """Test feet transformations w.r.t. base source frame.
 
-        In this test, the source frame is the robot base. This frame is at index 0, when
-        the frame bodies are sorted in the order of the regex matching in the frame transformer.
+        In this test, the source frame is the robot base.
         """
         # Spawn things into stage
         scene_cfg = MySceneCfg(num_envs=32, env_spacing=5.0, lazy_sensor_update=False)
@@ -141,9 +154,15 @@ def test_frame_transformer_feet_wrt_base(self):
         feet_indices, feet_names = scene.articulations["robot"].find_bodies(
             ["LF_FOOT", "RF_FOOT", "LH_FOOT", "RH_FOOT"]
         )
-        # Check names are parsed the same order
-        user_feet_names = [f"{name}_USER" for name in feet_names]
-        self.assertListEqual(scene.sensors["frame_transformer"].data.target_frame_names, user_feet_names)
+
+        target_frame_names = scene.sensors["frame_transformer"].data.target_frame_names
+
+        # Reorder the feet indices to match the order of the target frames with _USER suffix removed
+        target_frame_names = [name.split("_USER")[0] for name in target_frame_names]
+
+        # Find the indices of the feet in the order of the target frames
+        reordering_indices = [feet_names.index(name) for name in target_frame_names]
+        feet_indices = [feet_indices[i] for i in reordering_indices]
 
         # default joint targets
         default_actions = scene.articulations["robot"].data.default_joint_pos.clone()
@@ -185,6 +204,7 @@ def test_frame_transformer_feet_wrt_base(self):
             source_quat_w_tf = scene.sensors["frame_transformer"].data.source_quat_w
             feet_pos_w_tf = scene.sensors["frame_transformer"].data.target_pos_w
             feet_quat_w_tf = scene.sensors["frame_transformer"].data.target_quat_w
+
             # check if they are same
             torch.testing.assert_close(root_pose_w[:, :3], source_pos_w_tf, rtol=1e-3, atol=1e-3)
             torch.testing.assert_close(root_pose_w[:, 3:], source_quat_w_tf, rtol=1e-3, atol=1e-3)
@@ -302,6 +322,87 @@ def test_frame_transformer_feet_wrt_thigh(self):
                 torch.testing.assert_close(feet_pos_source_tf[:, index], foot_pos_b, rtol=1e-3, atol=1e-3)
                 torch.testing.assert_close(feet_quat_source_tf[:, index], foot_quat_b, rtol=1e-3, atol=1e-3)
 
+    def test_frame_transformer_body_wrt_cube(self):
+        """Test body transformation w.r.t. base source frame.
+
+        In this test, the source frame is the robot base.
+
+        The target_frame is a cube in the scene.
+        """
+        # Spawn things into stage
+        scene_cfg = MySceneCfg(num_envs=2, env_spacing=5.0, lazy_sensor_update=False)
+        scene_cfg.frame_transformer = FrameTransformerCfg(
+            prim_path="{ENV_REGEX_NS}/Robot/base",
+            target_frames=[
+                FrameTransformerCfg.FrameCfg(
+                    name="CUBE_USER",
+                    prim_path="{ENV_REGEX_NS}/cube",
+                ),
+            ],
+        )
+        scene = InteractiveScene(scene_cfg)
+
+        # Play the simulator
+        self.sim.reset()
+
+        # default joint targets
+        default_actions = scene.articulations["robot"].data.default_joint_pos.clone()
+        # Define simulation stepping
+        sim_dt = self.sim.get_physics_dt()
+        # Simulate physics
+        for count in range(100):
+            # # reset
+            if count % 25 == 0:
+                # reset root state
+                root_state = scene.articulations["robot"].data.default_root_state.clone()
+                root_state[:, :3] += scene.env_origins
+                joint_pos = scene.articulations["robot"].data.default_joint_pos
+                joint_vel = scene.articulations["robot"].data.default_joint_vel
+                # -- set root state
+                # -- robot
+                scene.articulations["robot"].write_root_state_to_sim(root_state)
+                scene.articulations["robot"].write_joint_state_to_sim(joint_pos, joint_vel)
+                # reset buffers
+                scene.reset()
+
+            # set joint targets
+            robot_actions = default_actions + 0.5 * torch.randn_like(default_actions)
+            scene.articulations["robot"].set_joint_position_target(robot_actions)
+            # write data to sim
+            scene.write_data_to_sim()
+            # perform step
+            self.sim.step()
+            # read data from sim
+            scene.update(sim_dt)
+
+            # check absolute frame transforms in world frame
+            # -- ground-truth
+            root_pose_w = scene.articulations["robot"].data.root_state_w[:, :7]
+            cube_pos_w_gt = scene.rigid_objects["cube"].data.root_state_w[:, :3]
+            cube_quat_w_gt = scene.rigid_objects["cube"].data.root_state_w[:, 3:7]
+            # -- frame transformer
+            source_pos_w_tf = scene.sensors["frame_transformer"].data.source_pos_w
+            source_quat_w_tf = scene.sensors["frame_transformer"].data.source_quat_w
+            cube_pos_w_tf = scene.sensors["frame_transformer"].data.target_pos_w.squeeze()
+            cube_quat_w_tf = scene.sensors["frame_transformer"].data.target_quat_w.squeeze()
+
+            # check if they are same
+            torch.testing.assert_close(root_pose_w[:, :3], source_pos_w_tf, rtol=1e-3, atol=1e-3)
+            torch.testing.assert_close(root_pose_w[:, 3:], source_quat_w_tf, rtol=1e-3, atol=1e-3)
+            torch.testing.assert_close(cube_pos_w_gt, cube_pos_w_tf, rtol=1e-3, atol=1e-3)
+            torch.testing.assert_close(cube_quat_w_gt, cube_quat_w_tf, rtol=1e-3, atol=1e-3)
+
+            # check if relative transforms are same
+            cube_pos_source_tf = scene.sensors["frame_transformer"].data.target_pos_source
+            cube_quat_source_tf = scene.sensors["frame_transformer"].data.target_quat_source
+            # ground-truth
+            cube_pos_b, cube_quat_b = math_utils.subtract_frame_transforms(
+                root_pose_w[:, :3], root_pose_w[:, 3:], cube_pos_w_tf, cube_quat_w_tf
+            )
+            # check if they are same
+            torch.testing.assert_close(cube_pos_source_tf[:, 0], cube_pos_b, rtol=1e-3, atol=1e-3)
+            torch.testing.assert_close(cube_quat_source_tf[:, 0], cube_quat_b, rtol=1e-3, atol=1e-3)
+
 
 if __name__ == "__main__":
     run_tests()