feat: add test tube pick task with custom assets and grasp annotations
- Add pick_test_tube task: USDC asset repackaging, grasp generation, task config - Add tools: usdc_to_obj.py, repackage_test_tube.py, fix_test_tube_materials.py - Add custom_task_guide.md: full Chinese documentation for creating custom tasks - Add crawled InternDataEngine online docs (23 pages) - Add grasp generation script (gen_tube_grasp.py) and pipeline config
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docs_crawled/config_dr.md
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# Source: https://internrobotics.github.io/InternDataEngine-Docs/config/dr.html
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# Domain Randomization [](#domain-randomization)
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Domain randomization generates diverse training data by varying simulation parameters. This helps bridge the sim-to-real gap and improves model generalization.
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## Randomization Types [](#randomization-types)
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### 1. Environment Map Randomization [](#_1-environment-map-randomization)
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Config Example:
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yaml
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```
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# Source: configs/manip/simbox/pick_and_place/lift2/single_pick/left/omniobject3d-banana.yaml
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env_map:
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envmap_lib: envmap_lib
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apply_randomization: True
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intensity_range: [4000, 7000]
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rotation_range: [0, 180]
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```
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Code Example:
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python
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```
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# Source: workflows/simbox/core/tasks/banana.py
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def _set_envmap(self):
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"""Randomize or reset the environment map (HDR dome light)."""
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cfg = self.cfg["env_map"]
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if cfg.get("light_type", "DomeLight") == "DomeLight":
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envmap_hdr_path_list = glob.glob(os.path.join(self.asset_root, cfg["envmap_lib"], "*.hdr"))
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envmap_hdr_path_list.sort()
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if cfg.get("apply_randomization", False):
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envmap_id = random.randint(0, len(envmap_hdr_path_list) - 1)
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intensity = random.uniform(cfg["intensity_range"][0], cfg["intensity_range"][1])
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rotation = [random.uniform(cfg["rotation_range"][0], cfg["rotation_range"][1]) for _ in range(3)]
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else:
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envmap_id = 0
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intensity = 1000.0
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rotation = [0.0, 0.0, 0.0]
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dome_prim_path = f"{self.root_prim_path}/DomeLight"
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envmap_hdr_path = envmap_hdr_path_list[envmap_id]
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if not is_prim_path_valid(dome_prim_path):
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self.dome_light_prim = UsdLux.DomeLight.Define(self.stage, dome_prim_path)
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UsdGeom.Xformable(self.dome_light_prim).AddRotateXYZOp().Set((rotation[0], rotation[1], rotation[2]))
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else:
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self.dome_light_prim.GetOrderedXformOps()[0].Set((rotation[0], rotation[1], rotation[2]))
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self.dome_light_prim.GetIntensityAttr().Set(intensity)
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self.dome_light_prim.GetTextureFileAttr().Set(envmap_hdr_path)
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```
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### 2. Texture Randomization [](#_2-texture-randomization)
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Config Example:
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yaml
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```
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# Source: workflows/simbox/core/configs/arenas/pick_randomized_arena.yaml
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fixtures:
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-
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name: table
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path: table0/instance.usd
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target_class: GeometryObject
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translation: [0.0, 0.0, 0.375]
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scale: [0.001, 0.001053, 0.001056]
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texture:
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texture_lib: "table_textures"
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apply_randomization: True
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texture_id: 0
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texture_scale: [0.001, 0.001]
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```
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Code Example:
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python
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```
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# Source: workflows/simbox/core/objects/plane_object.py
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def apply_texture(self, asset_root, cfg):
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texture_name = cfg["texture_lib"]
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texture_path_list = glob.glob(os.path.join(asset_root, texture_name, "*"))
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texture_path_list.sort()
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if cfg.get("apply_randomization", False):
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texture_id = random.randint(0, len(texture_path_list) - 1)
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else:
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texture_id = cfg["texture_id"]
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texture_path = texture_path_list[texture_id]
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mat_prim_path = f"{self.prim_path}/Looks/Material"
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if not is_prim_path_valid(mat_prim_path):
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self.mat = OmniPBR(
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prim_path=mat_prim_path,
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name="Material",
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texture_path=texture_path,
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texture_scale=cfg.get("texture_scale"),
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)
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self.apply_visual_material(self.mat)
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else:
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self.mat.set_texture(
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texture_path,
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)
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```
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### 3. Camera Pose Randomization [](#_3-camera-pose-randomization)
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Config Example:
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yaml
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```
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# Source: configs/manip/simbox/pick_and_place/lift2/single_pick/left/omniobject3d-banana.yaml
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cameras:
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-
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name: lift2_hand_left
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translation: [0.07, 0.01, 0.08]
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orientation: [0.62, 0.33, -0.33, -0.62]
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camera_axes: usd
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camera_file: workflows/simbox/core/configs/cameras/realsense_d405.yaml
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parent: "lift2/lift2/lift2/fl/link6" # todo
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apply_randomization: True
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max_translation_noise: 0.02
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max_orientation_noise: 2.5
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```
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Code Example:
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python
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```
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# Source: workflows/simbox/core/tasks/banana.py
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def _perturb_camera(self, camera, cfg, max_translation_noise=0.05, max_orientation_noise=10.0):
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translation = np.array(cfg["translation"])
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orientation = np.array(cfg["orientation"])
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random_direction = np.random.randn(3)
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random_direction /= np.linalg.norm(random_direction)
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random_distance = np.random.uniform(0, max_translation_noise)
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perturbed_translation = translation + random_direction * random_distance
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original_rot = R.from_quat(orientation, scalar_first=True)
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random_axis = np.random.randn(3)
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random_axis /= np.linalg.norm(random_axis)
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random_angle_deg = np.random.uniform(-max_orientation_noise, max_orientation_noise)
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random_angle_rad = np.radians(random_angle_deg)
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perturbation_rot = R.from_rotvec(random_axis * random_angle_rad)
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perturbed_rot = perturbation_rot * original_rot
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perturbed_orientation = perturbed_rot.as_quat(scalar_first=True)
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camera.set_local_pose(
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translation=perturbed_translation,
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orientation=perturbed_orientation,
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camera_axes=cfg["camera_axes"],
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)
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```
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### 4. Region Randomization [](#_4-region-randomization)
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Config Example:
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yaml
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```
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# Source: configs/manip/simbox/pick_and_place/lift2/single_pick/left/omniobject3d-banana.yaml
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regions:
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object: pick_object_left
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target: table
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random_type: A_on_B_region_sampler
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random_config:
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pos_range: [
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[-0.35, -0.25, 0.0],
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[0.05, 0.05, 0.0]
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]
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yaw_rotation: [-180.0, 180.0]
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-
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object: lift2
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target: table
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random_type: A_on_B_region_sampler
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random_config:
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pos_range: [
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[-0.025, -0.80, -0.775],
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[0.025, -0.80, -0.67]
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]
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yaw_rotation: [-0.0, 0.0]
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```
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Code Example:
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```
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# Source: workflows/simbox/core/tasks/banana.py
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def _set_regions(self):
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"""Randomize object poses according to region configs."""
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random_region_list = deepcopy(self.random_region_list)
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for cfg in self.cfg["regions"]:
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obj = self._task_objects[cfg["object"]]
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tgt = self._task_objects[cfg["target"]]
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if "sub_tgt_prim" in cfg:
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tgt = XFormPrim(prim_path=tgt.prim_path + cfg["sub_tgt_prim"])
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if "priority" in cfg:
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if cfg["priority"]:
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idx = random.choice(cfg["priority"])
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else:
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idx = random.randint(0, len(random_region_list) - 1)
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random_config = (random_region_list.pop(idx))["random_config"]
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sampler_fn = getattr(RandomRegionSampler, cfg["random_type"])
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pose = sampler_fn(obj, tgt, **random_config)
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obj.set_local_pose(*pose)
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elif "container" in cfg:
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container = self._task_objects[cfg["container"]]
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obj_trans = container.get_local_pose()[0]
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x_bias = random.choice(container.gap) if container.gap else 0
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obj_trans[0] += x_bias
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obj_trans[2] += cfg["z_init"]
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obj_ori = obj.get_local_pose()[1]
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obj.set_local_pose(obj_trans, obj_ori)
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elif "target2" in cfg:
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tgt2 = self._task_objects[cfg["target2"]]
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sampler_fn = getattr(RandomRegionSampler, cfg["random_type"])
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pose = sampler_fn(obj, tgt, tgt2, **cfg["random_config"])
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obj.set_local_pose(*pose)
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else:
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sampler_fn = getattr(RandomRegionSampler, cfg["random_type"])
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pose = sampler_fn(obj, tgt, **cfg["random_config"])
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obj.set_local_pose(*pose)
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# Source: workflows/simbox/core/utils/region_sampler.py
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@staticmethod
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def A_on_B_region_sampler(obj, tgt, pos_range, yaw_rotation):
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# Translation
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shift = np.random.uniform(*pos_range)
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bbox_obj = compute_bbox(obj.prim)
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obj_z_min = bbox_obj.min[2]
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bbox_tgt = compute_bbox(tgt.prim)
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tgt_center = (np.asarray(bbox_tgt.min) + np.asarray(bbox_tgt.max)) / 2
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tgt_z_max = bbox_tgt.max[2]
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place_pos = np.zeros(3)
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place_pos[0] = tgt_center[0]
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place_pos[1] = tgt_center[1]
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place_pos[2] = (
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tgt_z_max + (obj.get_local_pose()[0][2] - obj_z_min) + 0.001
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) # add a small value to avoid penetration
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place_pos += shift
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# Orientation
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yaw = np.random.uniform(*yaw_rotation)
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dr = R.from_euler("xyz", [0.0, 0.0, yaw], degrees=True)
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r = R.from_quat(obj.get_local_pose()[1], scalar_first=True)
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orientation = (dr * r).as_quat(scalar_first=True)
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return place_pos, orientation
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```
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## Key Files [](#key-files)
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- **utils/dr.py **( file ): Randomization utilities.
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- **utils/constants.py **( file ): Category parameters.
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- **tasks/banana.py **( file ): Randomization implementation.
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## References [](#references)
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- [Omni6DPose: A Benchmark and Model for Universal 6D Object Pose Estimation and Tracking (ECCV 2024)](https://jiyao06.github.io/Omni6DPose/)
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