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from std_msgs.msg import Header import sensor_msgs_py.point_cloud2 as pc2 + class VoxelGridNode(Node): def __init__(self): super().__init__('voxel_grid_node') - + self.rgb_sub = self.create_subscription( Image, '/camera/color/image_raw', self.rgb_callback, 10) self.depth_sub = self.create_subscription(

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CameraInfo, '/camera/depth/camera_info', self.info_callback, 10) self.voxel_pub = self.create_publisher(PointCloud2, '/voxel_grid', 10) - + self.rgb_image = None self.depth_image = None self.bridge = CvBridge() - + # Camera intrinsics (ovewrritten for your camera) self.fx, self.fy, self.cx, self.cy = 525.0, 525.0, 320.0, 240.0 - - self.voxel_size = 0.04 - self.max_range = 3.5 - + + self.voxel_size = 0.04 + self.max_range = 3.5 + self.get_logger().info('Voxel Grid Node started') def info_callback(self, msg):

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def process_rgbd_if_ready(self): if self.rgb_image is None or self.depth_image is None: return - + # Convert RGBD to point cloud points = self.rgbd_to_pointcloud(self.rgb_image, self.depth_image) - + if len(points) > 0: # Create voxel grid voxel_centers = self.create_voxel_grid(points) - + # Publish voxel grid as point cloud self.publish_voxel_grid(voxel_centers) def rgbd_to_pointcloud(self, rgb, depth): points = [] h, w = depth.shape - - for v in range(0, h, 4): #Skipping every 4 pixels to downsample, can change if nessary + + for v in range(0, h, 4): # Skipping every 4 pixels to downsample, can change if nessary for u in range(0, w, 4): - z = depth[v, u] / 1000.0 - - if z > 0.1 and z < self.max_range: + z = depth[v, u] / 1000.0 + + if z > 0.1 and z < self.max_range: x = (u - self.cx) * z / self.fx y = (v - self.cy) * z / self.fy points.append([x, y, z]) - + return np.array(points, dtype=np.float32) def create_voxel_grid(self, points): points_scaled = points / self.voxel_size - + min_coords = points_scaled.min(axis=0) max_coords = points_scaled.max(axis=0) padding = 2.0 - + coors_range = [ min_coords[0] - padding, min_coords[1] - padding, min_coords[2] - padding, max_coords[0] + padding, max_coords[1] + padding, max_coords[2] + padding ] - + voxel_gen = PointToVoxel( - vsize_xyz=[1.0, 1.0, 1.0], + vsize_xyz=[1.0, 1.0, 1.0], coors_range_xyz=coors_range, num_point_features=3, max_num_voxels=10000, max_num_points_per_voxel=20 ) - + pc_tensor = torch.from_numpy(points_scaled) voxels, indices, num_points_per_voxel = voxel_gen(pc_tensor) - - voxel_centers = indices.numpy() * self.voxel_size + np.array(coors_range[:3]) * self.voxel_size + self.voxel_size/2 - + + voxel_centers = indices.numpy() * self.voxel_size + \ + np.array(coors_range[:3]) * self.voxel_size + self.voxel_size/2 + self.get_logger().info(f'Created {len(voxels)} voxels from {len(points)} points') - + return voxel_centers def publish_voxel_grid(self, voxel_centers): header = Header() header.stamp = self.get_clock().now().to_msg() header.frame_id = 'camera_link' - + fields = [ PointField(name='x', offset=0, datatype=PointField.FLOAT32, count=1), PointField(name='y', offset=4, datatype=PointField.FLOAT32, count=1), PointField(name='z', offset=8, datatype=PointField.FLOAT32, count=1), ] - + pc_msg = pc2.create_cloud(header, fields, voxel_centers) self.voxel_pub.publish(pc_msg) + def main(args=None): rclpy.init(args=args) node = VoxelGridNode() - + try: rclpy.spin(node) except KeyboardInterrupt:

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node.destroy_node() rclpy.shutdown() + if __name__ == '__main__': - main() \ No newline at end of file + main()

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import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D + def load_bunny_data(): with open('bunnyData.pts', 'r') as f: lines = f.readlines() - + num_points = int(lines[0].strip()) points = [] - + for i in range(1, min(num_points + 1, len(lines))): line = lines[i].strip() if line: coords = list(map(float, line.split())) if len(coords) >= 3: points.append(coords[:3]) - + points = np.array(points, dtype=np.float32) print(f"Loaded {len(points)} points from bunnyData.pts") print(f"Range: X[{points[:, 0].min():.3f}, {points[:, 0].max():.3f}] " f"Y[{points[:, 1].min():.3f}, {points[:, 1].max():.3f}] " f"Z[{points[:, 2].min():.3f}, {points[:, 2].max():.3f}]") - + return points + def create_voxel_grid(points, voxel_size=2.0): """Convert point cloud to voxel grid""" # Scale to mm for better voxel resolution points_scaled = points * 1000 - + min_coords = points_scaled.min(axis=0) max_coords = points_scaled.max(axis=0) padding = 5.0 - + coors_range = [ min_coords[0] - padding, min_coords[1] - padding, min_coords[2] - padding, max_coords[0] + padding, max_coords[1] + padding, max_coords[2] + padding ] - + # Create voxel generator voxel_gen = PointToVoxel( vsize_xyz=[voxel_size, voxel_size, voxel_size],

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max_num_voxels=20000, max_num_points_per_voxel=50 ) - + # Generate voxels pc_tensor = torch.from_numpy(points_scaled) voxels, indices, num_points_per_voxel = voxel_gen(pc_tensor) - + # Convert voxel indices to world coordinates voxel_centers = indices.numpy() * voxel_size + np.array(coors_range[:3]) + voxel_size/2 - + print(f"Created {len(voxels)} voxels from {len(points)} points") print(f"Voxel size: {voxel_size}mm") - + return voxel_centers, voxels, indices + def visualize_voxels(voxel_centers): """Create 3D visualization of voxel grid""" fig = plt.figure(figsize=(12, 10)) ax = fig.add_subplot(111, projection='3d') - + # Color by Z-coordinate colors = voxel_centers[:, 2] - + scatter = ax.scatter(voxel_centers[:, 0], voxel_centers[:, 1], voxel_centers[:, 2], - c=colors, cmap='viridis', s=30, alpha=0.8) - + c=colors, cmap='viridis', s=30, alpha=0.8) + # Add colorbar cbar = plt.colorbar(scatter, ax=ax, shrink=0.5) cbar.set_label('Z Coordinate (mm)') - + ax.set_xlabel('X (mm)') ax.set_ylabel('Y (mm)') ax.set_zlabel('Z (mm)') ax.set_title('Voxel Grid Visualization') ax.grid(True, alpha=0.3) ax.view_init(elev=20, azim=45) - + plt.savefig('voxel_visualization.png', dpi=150, bbox_inches='tight') print("Saved visualization as 'voxel_visualization.png'") plt.show() + def test_basic_functionality(): """Test basic voxel generation with synthetic data""" print("Testing basic voxel functionality...") - + # Generate random point cloud np.random.seed(42) points = np.random.uniform(-2, 2, size=[1000, 3]).astype(np.float32) - + voxel_gen = PointToVoxel( vsize_xyz=[0.1, 0.1, 0.1], coors_range_xyz=[-3, -3, -3, 3, 3, 3],

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max_num_voxels=5000, max_num_points_per_voxel=10 ) - + pc_tensor = torch.from_numpy(points) voxels, indices, num_points = voxel_gen(pc_tensor) - + print(f"Input: {len(points)} points") print(f"Output: {len(voxels)} voxels") print(f"Voxel shape: {voxels.shape}") print(f"Average points per voxel: {num_points.float().mean():.2f}") - + return True + def main(): """Main execution flow""" print("Voxel Grid Processing") print("-" * 40) - + # Test basic functionality first test_basic_functionality() print() - + # Process bunny data if available try: points = load_bunny_data()

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except Exception as e: print(f"Error processing bunny data: {e}") + if __name__ == "__main__": - main() - \ No newline at end of file + main()

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Import("env") env.Append( - LINKFLAGS=[ - "-mfpu=fpv4-sp-d16", - "-mfloat-abi=hard" - ] -) \ No newline at end of file + LINKFLAGS=[ + "-mfpu=fpv4-sp-d16", + "-mfloat-abi=hard" + ] +)

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env_name = env["PIOENV"] -env.Replace(PROGNAME=env_name) \ No newline at end of file +env.Replace(PROGNAME=env_name)

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'dummy_publisher_node = perception.dummy_publisher_node:main' ], }, -) \ No newline at end of file +)

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# Add slight depth variation (noise) depth_noise = np.random.randint(-30, 30, depth_image.shape, dtype=np.int16) - depth_image = np.clip(depth_image.astype(np.int32) + depth_noise, 0, 65535).astype(np.uint16) + depth_image = np.clip(depth_image.astype(np.int32) + + depth_noise, 0, 65535).astype(np.uint16) rgb_msg = self.bridge.cv2_to_imgmsg( rgb_image, encoding='bgr8'

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default_value='info', description='Log level for the dummy publisher node' ), - + Node( package='perception', executable='dummy_publisher_node',

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# Add any topic remappings here if needed ] ) - ]) \ No newline at end of file + ])

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'voxel_grid_node = voxel_grid.voxel_grid_node:main' ], }, -) \ No newline at end of file +)

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self.fx, self.fy, self.cx, self.cy = 525.0, 525.0, 320.0, 240.0 self.have_intrinsics = False - self.voxel_size = 0.04 - self.max_range = 3.5 - self.min_range = 0.1 - self.stride = 4 + self.voxel_size = 0.04 + self.max_range = 3.5 + self.min_range = 0.1 + self.stride = 4 self.get_logger().info('Voxel Grid Node started (world modelling, headless)')

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self.cy = float(msg.k[5]) self.have_intrinsics = True - self.get_logger().info(f'Intrinsics updated: fx={self.fx:.2f}, fy={self.fy:.2f}, cx={self.cx:.2f}, cy={self.cy:.2f}') + self.get_logger().info( + f'Intrinsics updated: fx={self.fx:.2f}, fy={self.fy:.2f}, cx={self.cx:.2f}, cy={self.cy:.2f}') def rgb_callback(self, msg: Image): self.rgb_image = self._decode_rgb_bgr8(msg)

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self.publish_voxel_grid(voxel_centers) - #reset + # reset self.depth_image = None self.rgb_image = None

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voxel_gen = PointToVoxel( vsize_xyz=[self.voxel_size, self.voxel_size, self.voxel_size], - coors_range_xyz=coors_range_m.tolist(), #Convert numpy to python + coors_range_xyz=coors_range_m.tolist(), # Convert numpy to python num_point_features=3, max_num_voxels=10000, max_num_points_per_voxel=20 ) pc_tensor = torch.from_numpy(points) # float32 - voxels, indices, num_points = voxel_gen(pc_tensor) # indices: [M, 3] (z,y,x) or (x,y,z) depending on impl + # indices: [M, 3] (z,y,x) or (x,y,z) depending on impl + voxels, indices, num_points = voxel_gen(pc_tensor) if indices.numel() == 0: return np.zeros((0, 3), dtype=np.float32)

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def publish_voxel_grid(self, voxel_centers: np.ndarray): header = Header() header.stamp = self.get_clock().now().to_msg() - header.frame_id = 'camera_link' + header.frame_id = 'camera_link' fields = [ PointField(name='x', offset=0, datatype=PointField.FLOAT32, count=1),

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executable='voxel_grid_node', output='screen', emulate_tty=True), - ]) \ No newline at end of file + ])

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'octo_map_node = octo_map.octo_map_node:main' ], }, -) \ No newline at end of file +)

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from std_msgs.msg import Header import sensor_msgs_py.point_cloud2 as pc2 + class OctoMapNode(Node): def __init__(self): super().__init__('octo_map_node') - + # Subscribe to processed perception data self.rgb_sub = self.create_subscription( Image, '/perception/rgb_processed', self.rgb_callback, 10)

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CameraInfo, '/perception/camera_info', self.info_callback, 10) self.octo_pub = self.create_publisher(PointCloud2, '/behaviour/octo_map', 10) - + self.rgb_image = None self.depth_image = None self.bridge = CvBridge() - + # Camera intrinsics (will be updated from camera info) self.fx, self.fy, self.cx, self.cy = 525.0, 525.0, 320.0, 240.0 - - self.voxel_size = 0.04 - self.max_range = 3.5 - + + self.voxel_size = 0.04 + self.max_range = 3.5 + self.get_logger().info('OctoMap Node started - subscribing to processed perception data') def info_callback(self, msg):

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# TODO: Implement point cloud generation and octomap updating - + def main(args=None): rclpy.init(args=args) node = OctoMapNode() - + try: rclpy.spin(node) except KeyboardInterrupt:

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node.destroy_node() rclpy.shutdown() + if __name__ == '__main__': - main() \ No newline at end of file + main()

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executable='octo_map_node', output='screen', emulate_tty=True), - ]) \ No newline at end of file + ])

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import rclpy from rclpy.node import Node from std_msgs.msg import Float32MultiArray + class HandPoseSubscriber(Node): def __init__(self):

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self.joint_positions = list(msg.data) self.get_logger().info(f'Received joint positions: {self.joint_positions}') else: - self.get_logger().warn(f'Received invalid joint positions: expected 15 floats, got {len(msg.data)}') + self.get_logger().warn( + f'Received invalid joint positions: expected 15 floats, got {len(msg.data)}') def get_latest_joint_positions(self): return self.joint_positions + def main(args=None): rclpy.init(args=args)

node.destroy_node() rclpy.shutdown() + if __name__ == '__main__': - main() \ No newline at end of file + main()

+from test_subscriber import HandPoseSubscriber +from test_publisher import TestFloatPublisher +from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR +from HumanoidRL.HumanoidRLPackage.HumanoidRLSetup.modelCfg.humanoid import HAND_CFG +from isaaclab.utils import configclass +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.managers import SceneEntityCfg +from isaaclab.assets import AssetBaseCfg +import isaaclab.sim as sim_utils +import rclpy +import torch import argparse from isaaclab.app import AppLauncher

app_launcher = AppLauncher(args_cli) simulation_app = app_launcher.app -import torch -import rclpy - -import isaaclab.sim as sim_utils -from isaaclab.assets import AssetBaseCfg -from isaaclab.managers import SceneEntityCfg -from isaaclab.scene import InteractiveScene, InteractiveSceneCfg -from isaaclab.utils import configclass - -from HumanoidRL.HumanoidRLPackage.HumanoidRLSetup.modelCfg.humanoid import HAND_CFG -from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR -from test_publisher import TestFloatPublisher -from test_subscriber import HandPoseSubscriber @configclass class HandSceneCfg(InteractiveSceneCfg):

) robot = HAND_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") + def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene, node, test_node): robot = scene["robot"]

robot.set_joint_position_target(joint_pos_des, joint_ids=robot_entity_cfg.joint_ids) else: print("[INFO]: No valid joint values received, using default positions.") - joint_position_list = [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]] + joint_position_list = [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, + 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]] joint_position = torch.tensor(joint_position_list[0], device=sim.device) robot.reset() joint_pos_des = joint_position.unsqueeze(0)[:, robot_entity_cfg.joint_ids].clone() robot.set_joint_position_target(joint_pos_des, joint_ids=robot_entity_cfg.joint_ids) - scene.write_data_to_sim() sim.step() scene.update(sim_dt) + def main(): # Initialize ROS2

rclpy.shutdown() simulation_app.close() + if __name__ == "__main__": - main() \ No newline at end of file + main()

import rclpy from rclpy.node import Node from std_msgs.msg import Float32MultiArray + class TestFloatPublisher(Node): def __init__(self):

self.publisher_.publish(msg) self.get_logger().info(f'Published joint positions: {msg.data}') + def main(args=None): rclpy.init(args=args) node = TestFloatPublisher()

node.destroy_node() rclpy.shutdown() + if __name__ == '__main__': - main() \ No newline at end of file + main()

+from isaaclab_assets import UR10_CFG +from isaaclab.utils.math import subtract_frame_transforms +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.utils import configclass +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.markers.config import FRAME_MARKER_CFG +from isaaclab.markers import VisualizationMarkers +from isaaclab.managers import SceneEntityCfg +from isaaclab.controllers import DifferentialIKController, DifferentialIKControllerCfg +from isaaclab.assets import AssetBaseCfg +import isaaclab.sim as sim_utils +import torch import argparse from isaaclab.app import AppLauncher

app_launcher = AppLauncher(args_cli) simulation_app = app_launcher.app -import torch - -import isaaclab.sim as sim_utils -from isaaclab.assets import AssetBaseCfg -from isaaclab.controllers import DifferentialIKController, DifferentialIKControllerCfg -from isaaclab.managers import SceneEntityCfg -from isaaclab.markers import VisualizationMarkers -from isaaclab.markers.config import FRAME_MARKER_CFG -from isaaclab.scene import InteractiveScene, InteractiveSceneCfg -from isaaclab.utils import configclass -from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR -from isaaclab.utils.math import subtract_frame_transforms ## # Pre-defined configs ## -from isaaclab_assets import UR10_CFG @configclass

def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): - + robot = scene["robot"] # Create controller - diff_ik_cfg = DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls") - diff_ik_controller = DifferentialIKController(diff_ik_cfg, num_envs=scene.num_envs, device=sim.device) + diff_ik_cfg = DifferentialIKControllerCfg( + command_type="pose", use_relative_mode=False, ik_method="dls") + diff_ik_controller = DifferentialIKController( + diff_ik_cfg, num_envs=scene.num_envs, device=sim.device) # Markers frame_marker_cfg = FRAME_MARKER_CFG.copy()

[1.0, -1.712, 1.712, 0.0, 0.0, 0.0], [0.0, 1.712, 1.712, 0.0, 0.0, 0.0], ] - joint_position = torch.tensor(joint_position_list[joint_position_index], device=sim.device) + joint_position = torch.tensor( + joint_position_list[joint_position_index], device=sim.device) joint_vel = robot.data.default_joint_vel.clone() if joint_position_index >= len(joint_position_list) - 1: joint_position_index = 0 else: joint_position_index += 1 - + robot.reset() joint_pos_des = joint_position.unsqueeze(0)[:, robot_entity_cfg.joint_ids].clone()

if __name__ == "__main__": main() - simulation_app.close() \ No newline at end of file + simulation_app.close()

+from isaaclab_assets import UR10_CFG, FRANKA_PANDA_HIGH_PD_CFG +from isaaclab.utils.math import subtract_frame_transforms +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.utils import configclass +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.markers.config import FRAME_MARKER_CFG +from isaaclab.markers import VisualizationMarkers +from isaaclab.managers import SceneEntityCfg +from isaaclab.controllers import DifferentialIKController, DifferentialIKControllerCfg +from isaaclab.assets import AssetBaseCfg +import isaaclab.sim as sim_utils +import torch import argparse from isaaclab.app import AppLauncher

app_launcher = AppLauncher(args_cli) simulation_app = app_launcher.app -import torch - -import isaaclab.sim as sim_utils -from isaaclab.assets import AssetBaseCfg -from isaaclab.controllers import DifferentialIKController, DifferentialIKControllerCfg -from isaaclab.managers import SceneEntityCfg -from isaaclab.markers import VisualizationMarkers -from isaaclab.markers.config import FRAME_MARKER_CFG -from isaaclab.scene import InteractiveScene, InteractiveSceneCfg -from isaaclab.utils import configclass -from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR -from isaaclab.utils.math import subtract_frame_transforms ## # Pre-defined configs ## -from isaaclab_assets import UR10_CFG, FRANKA_PANDA_HIGH_PD_CFG @configclass

def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): - + robot = scene["robot"] # Create controller - diff_ik_cfg = DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls") - diff_ik_controller = DifferentialIKController(diff_ik_cfg, num_envs=scene.num_envs, device=sim.device) + diff_ik_cfg = DifferentialIKControllerCfg( + command_type="pose", use_relative_mode=False, ik_method="dls") + diff_ik_controller = DifferentialIKController( + diff_ik_cfg, num_envs=scene.num_envs, device=sim.device) # Markers frame_marker_cfg = FRAME_MARKER_CFG.copy()

# Specify robot-specific parameters if args_cli.robot == "franka_panda": - robot_entity_cfg = SceneEntityCfg("robot", joint_names=["panda_joint.*"], body_names=["panda_hand"]) + robot_entity_cfg = SceneEntityCfg( + "robot", joint_names=["panda_joint.*"], body_names=["panda_hand"]) elif args_cli.robot == "ur10": robot_entity_cfg = SceneEntityCfg("robot", joint_names=[".*"], body_names=["ee_link"]) else:

while simulation_app.is_running(): # Get cube/target_point coordinates position, quaternion = scene["cube"].get_world_poses() - ik_commands = torch.cat([position, quaternion], dim=1) # Quaternion is in (w, x, y, z) + ik_commands = torch.cat([position, quaternion], dim=1) # Quaternion is in (w, x, y, z) diff_ik_controller.set_command(ik_commands) # obtain quantities from simulation - jacobian = robot.root_physx_view.get_jacobians()[:, ee_jacobi_idx, :, robot_entity_cfg.joint_ids] + jacobian = robot.root_physx_view.get_jacobians( + )[:, ee_jacobi_idx, :, robot_entity_cfg.joint_ids] ee_pose_w = robot.data.body_state_w[:, robot_entity_cfg.body_ids[0], 0:7] root_pose_w = robot.data.root_state_w[:, 0:7] joint_pos = robot.data.joint_pos[:, robot_entity_cfg.joint_ids]

if __name__ == "__main__": main() - simulation_app.close() \ No newline at end of file + simulation_app.close()

+from isaaclab_assets import SHADOW_HAND_CFG +from isaaclab.utils.math import subtract_frame_transforms +from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR +from isaaclab.utils import configclass +from isaaclab.scene import InteractiveScene, InteractiveSceneCfg +from isaaclab.markers.config import FRAME_MARKER_CFG +from isaaclab.markers import VisualizationMarkers +from isaaclab.managers import SceneEntityCfg +from isaaclab.controllers import DifferentialIKController, DifferentialIKControllerCfg +from isaaclab.assets import AssetBaseCfg +import isaaclab.sim as sim_utils +import torch import argparse from isaaclab.app import AppLauncher

Using main as the target branch

Using voxel-grid as the source branch

MODIFIED_MODULES are perception infrastructure

Detected infrastructure changes