curobo.geom.sdf.world_blox module

class WorldBloxCollision(
config: WorldCollisionConfig,
)

Bases: WorldVoxelCollision

World Collision Representaiton using Nvidia’s nvblox library.

This class depends on pytorch wrapper for nvblox. Additionally, this representation does not support batched environments as we only store one world via nvblox.

There are two ways to use nvblox, one is by loading maps from disk and the other is by creating maps online. In both these instances, we might load more than one map and need to check collisions against all maps.

To facilitate online map creation and updation, we build apis in this class that provide.

load_collision_model(
world_model: WorldConfig,
fix_cache_reference: bool = False,
)
clear_cache()
clear_blox_layer(
layer_name: str,
)
_get_blox_sdf(
query_spheres,
collision_query_buffer: CollisionQueryBuffer,
weight,
activation_distance,
return_loss: bool = False,
compute_esdf: bool = False,
)
_get_blox_swept_sdf(
query_spheres,
collision_query_buffer,
weight,
activation_distance,
speed_dt,
sweep_steps,
enable_speed_metric,
return_loss=False,
)
get_sphere_distance(
query_sphere: Tensor,
collision_query_buffer: CollisionQueryBuffer,
weight: Tensor,
activation_distance: Tensor,
env_query_idx: Tensor | None = None,
return_loss: bool = False,
sum_collisions: bool = True,
compute_esdf: bool = False,
)

Computes the signed distance via analytic function Args: tensor_sphere: b, n, 4

get_sphere_collision(
query_sphere,
collision_query_buffer: CollisionQueryBuffer,
weight: Tensor,
activation_distance: Tensor,
env_query_idx=None,
return_loss: bool = False,
**kwargs,
)

Computes the signed distance via analytic function Args: tensor_sphere: b, n, 4 we assume we don’t need gradient for this function. If you need gradient, use get_sphere_distance

get_swept_sphere_distance(
query_sphere,
collision_query_buffer: CollisionQueryBuffer,
weight: Tensor,
activation_distance: Tensor,
speed_dt: Tensor,
sweep_steps: int,
enable_speed_metric=False,
env_query_idx: Tensor | None = None,
return_loss: bool = False,
sum_collisions: bool = True,
)

Computes the signed distance via analytic function Args: tensor_sphere: b, n, 4

get_swept_sphere_collision(
query_sphere,
collision_query_buffer: CollisionQueryBuffer,
weight: Tensor,
sweep_steps,
activation_distance: Tensor,
speed_dt: Tensor,
enable_speed_metric=False,
env_query_idx: Tensor | None = None,
return_loss: bool = False,
)

Computes the signed distance via analytic function Args: tensor_sphere: b, n, 4

enable_obstacle(
name: str,
enable: bool = True,
env_idx: int = 0,
)
enable_blox(
enable: bool = True,
name: str | None = None,
)
update_blox_pose(
w_obj_pose: Pose | None = None,
obj_w_pose: Pose | None = None,
name: str | None = None,
)
clear_bounding_box(
cuboid: Cuboid,
layer_name: str | None = None,
)
get_bounding_spheres(
bounding_box: Cuboid,
obstacle_name: str | None = None,
n_spheres: int = 1,
surface_sphere_radius: float = 0.002,
fit_type: SphereFitType = SphereFitType.VOXEL_VOLUME_SAMPLE_SURFACE,
voxelize_method: str = 'ray',
pre_transform_pose: Pose | None = None,
clear_region: bool = False,
) → List[Sphere]
add_camera_frame(
camera_observation: CameraObservation,
layer_name: str,
)
process_camera_frames(
layer_name: str | None = None,
process_aux: bool = False,
)
update_blox_hashes()
update_blox_esdf(
layer_name: str | None = None,
)
update_blox_mesh(
layer_name: str | None = None,
)
get_mesh_from_blox_layer(
layer_name: str,
mode: str = 'nvblox',
) → Mesh
save_layer(
layer_name: str,
file_name: str,
) → bool
decay_layer(
layer_name: str,
)
_batch_tensor_voxel(
pose: List[List[float]],
dims: List[float],
voxel_size: List[float],
)
_create_mesh_cache(
mesh_cache,
)
_create_obb_cache(
obb_cache,
)
_create_voxel_cache(
voxel_cache: Dict[str, Any],
)
classmethod _get_obstacle_poses(
w_obj_pose: Pose | None = None,
obj_w_pose: Pose | None = None,
)
_get_sdf(
query_spheres,
collision_query_buffer: CollisionQueryBuffer,
weight: Tensor,
activation_distance: Tensor,
env_query_idx=None,
return_loss=False,
compute_esdf=False,
)
_get_swept_sdf(
query_spheres,
collision_query_buffer: CollisionQueryBuffer,
weight: Tensor,
activation_distance: Tensor,
speed_dt: Tensor,
sweep_steps: int,
enable_speed_metric=False,
env_query_idx=None,
return_loss: bool = False,
)
_init_cache()
_load_batch_mesh_to_warp(
mesh_list: List[Mesh],
)
_load_collision_model_in_cache(
world_config: WorldConfig,
env_idx: int = 0,
fix_cache_reference: bool = False,
)
_load_mesh_into_cache(
mesh: Mesh,
) → WarpMeshData
_load_mesh_to_warp(
mesh: Mesh,
)
_load_voxel_collision_model_in_cache(
world_config: WorldConfig,
env_idx: int = 0,
fix_cache_reference: bool = False,
)

TODO:

_extended_summary_

Parameters:

  • world_config – _description_

  • env_idx – _description_

  • fix_cache_reference – _description_

add_mesh(
new_mesh: Mesh,
env_idx: int = 0,
)
add_obb(
cuboid: Cuboid,
env_idx: int = 0,
)
add_obb_from_raw(
name: str,
dims: Tensor,
env_idx: int,
w_obj_pose: Pose | None = None,
obj_w_pose: Pose | None = None,
)

Args: dims: lenght, width, height position: x,y,z rotation: matrix (3x3)

cache: Dict[Obstacle, int] | None = None
checker_type: CollisionCheckerType = 'PRIMITIVE'
clear_voxelization_cache()
create_collision_cache(
mesh_cache=None,
obb_cache=None,
n_envs=None,
)
enable_mesh(
enable: bool = True,
name: str | None = None,
env_mesh_idx: Tensor | None = None,
env_idx: int = 0,
)

Update obstacle dimensions

Parameters:
enable_obb(
enable: bool = True,
name: str | None = None,
env_obj_idx: Tensor | None = None,
env_idx: int = 0,
)

Update obstacle dimensions

Parameters:
enable_voxel(
enable: bool = True,
name: str | None = None,
env_obj_idx: Tensor | None = None,
env_idx: int = 0,
)

Update obstacle dimensions

Parameters:
get_esdf_in_bounding_box(
cuboid: Cuboid = Cuboid(name='test', pose=[0, 0, 0, 1, 0, 0, 0], scale=None, color=None, texture_id=None, texture=None, material=Material(metallic=0.0, roughness=0.4), tensor_args=TensorDeviceType(device=device(type='cuda', index=0), dtype=torch.float32, collision_geometry_dtype=torch.float32, collision_gradient_dtype=torch.float32, collision_distance_dtype=torch.float32), dims=[1, 1, 1]),
voxel_size: float = 0.02,
dtype=torch.float32,
) → VoxelGrid
get_mesh_idx(
name: str,
env_idx: int = 0,
) → int
get_mesh_in_bounding_box(
cuboid: Cuboid = Cuboid(name='test', pose=[0, 0, 0, 1, 0, 0, 0], scale=None, color=None, texture_id=None, texture=None, material=Material(metallic=0.0, roughness=0.4), tensor_args=TensorDeviceType(device=device(type='cuda', index=0), dtype=torch.float32, collision_geometry_dtype=torch.float32, collision_gradient_dtype=torch.float32, collision_distance_dtype=torch.float32), dims=[1, 1, 1]),
voxel_size: float = 0.02,
) → Mesh
get_obb_idx(
name: str,
env_idx: int = 0,
) → int
get_occupancy_in_bounding_box(
cuboid: Cuboid = Cuboid(name='test', pose=[0, 0, 0, 1, 0, 0, 0], scale=None, color=None, texture_id=None, texture=None, material=Material(metallic=0.0, roughness=0.4), tensor_args=TensorDeviceType(device=device(type='cuda', index=0), dtype=torch.float32, collision_geometry_dtype=torch.float32, collision_gradient_dtype=torch.float32, collision_distance_dtype=torch.float32), dims=[1, 1, 1]),
voxel_size: float = 0.02,
) → VoxelGrid
get_sphere_trace(
query_sphere,
collision_query_buffer: CollisionQueryBuffer,
weight: Tensor,
sweep_steps: int,
env_query_idx: Tensor | None = None,
return_loss: bool = False,
)
get_voxel_grid(
name: str,
env_idx: int = 0,
)
get_voxel_grid_shape(
env_idx: int = 0,
obs_idx: int = 0,
)
get_voxel_idx(
name: str,
env_idx: int = 0,
) → int
get_voxels_in_bounding_box(
cuboid: Cuboid = Cuboid(name='test', pose=[0, 0, 0, 1, 0, 0, 0], scale=None, color=None, texture_id=None, texture=None, material=Material(metallic=0.0, roughness=0.4), tensor_args=TensorDeviceType(device=device(type='cuda', index=0), dtype=torch.float32, collision_geometry_dtype=torch.float32, collision_gradient_dtype=torch.float32, collision_distance_dtype=torch.float32), dims=[1, 1, 1]),
voxel_size: float = 0.02,
) → List[Cuboid] | Tensor
load_batch_collision_model(
world_config_list: List[WorldConfig],
)

Load voxel grid for batched environments

_extended_summary_

Parameters:

world_config_list – _description_

Returns:

_description_

static load_from_dict(
world_coll_checker_dict: Dict,
world_model_dict: WorldConfig | Dict | List[WorldConfig] | None = None,
tensor_args: TensorDeviceType = TensorDeviceType(device=device(type='cuda', index=0), dtype=torch.float32, collision_geometry_dtype=torch.float32, collision_gradient_dtype=torch.float32, collision_distance_dtype=torch.float32),
)
max_distance: torch.Tensor | float = 0.1
max_esdf_distance: torch.Tensor | float = 100.0
n_envs: int = 1
update_all_mesh_pose(
w_obj_pose: Pose | None = None,
obj_w_pose: Pose | None = None,
name: List[str] | None = None,
env_obj_idx: Tensor | None = None,
env_idx: int = 0,
)

Update poses for a list of meshes in the same environment

Parameters:

  • w_obj_pose (Optional[Pose], optional) – _description_. Defaults to None.

  • obj_w_pose (Optional[Pose], optional) – _description_. Defaults to None.

  • name (Optional[List[str]], optional) – _description_. Defaults to None.

  • env_obj_idx (Optional[torch.Tensor], optional) – _description_. Defaults to None.

  • env_idx (int, optional) – _description_. Defaults to 0.

update_cache_voxelization(
new_grid: VoxelGrid,
)
update_mesh_from_warp(
warp_mesh_idx: int,
w_obj_pose: Pose | None = None,
obj_w_pose: Pose | None = None,
obj_idx: int = 0,
env_idx: int = 0,
name: str | None = None,
)
update_mesh_pose(
w_obj_pose: Pose | None = None,
obj_w_pose: Pose | None = None,
name: str | None = None,
env_obj_idx: Tensor | None = None,
env_idx: int = 0,
)
update_mesh_pose_env(
w_obj_pose: Pose | None = None,
obj_w_pose: Pose | None = None,
name: str | None = None,
env_obj_idx: Tensor | None = None,
env_idx: List[int] = [0],
)

Update pose of a single object in a list of environments

Parameters:

  • w_obj_pose (Optional[Pose], optional) – _description_. Defaults to None.

  • obj_w_pose (Optional[Pose], optional) – _description_. Defaults to None.

  • name (Optional[List[str]], optional) – _description_. Defaults to None.

  • env_obj_idx (Optional[torch.Tensor], optional) – _description_. Defaults to None.

  • env_idx (List[int], optional) – _description_. Defaults to [0].

update_obb_dims(
obj_dims: Tensor,
name: str | None = None,
env_obj_idx: Tensor | None = None,
env_idx: int = 0,
)

Update obstacle dimensions

Parameters:
update_obb_pose(
w_obj_pose: Pose | None = None,
obj_w_pose: Pose | None = None,
name: str | None = None,
env_obj_idx: Tensor | None = None,
env_idx: int = 0,
)

Update pose of a specific objects. This also updates the signed distance grid to account for the updated object pose. Args: obj_w_pose: Pose obj_idx:

update_obstacle_pose(
name: str,
w_obj_pose: Pose,
env_idx: int = 0,
)
update_voxel_data(
new_voxel: VoxelGrid,
env_idx: int = 0,
)
update_voxel_features(
features: Tensor,
name: str | None = None,
env_obj_idx: Tensor | None = None,
env_idx: int = 0,
)

Update pose of a specific objects. This also updates the signed distance grid to account for the updated object pose. Args: obj_w_pose: Pose obj_idx:

update_voxel_pose(
w_obj_pose: Pose | None = None,
obj_w_pose: Pose | None = None,
name: str | None = None,
env_obj_idx: Tensor | None = None,
env_idx: int = 0,
)

Update pose of a specific objects. This also updates the signed distance grid to account for the updated object pose. Args: obj_w_pose: Pose obj_idx:

world_model: List[WorldConfig] | WorldConfig | None = None
tensor_args: TensorDeviceType