curobo.graph.graph_base module

class GraphResult( success: torch.Tensor, start_q: torch.Tensor, goal_q: torch.Tensor, path_length: torch.Tensor | None = None, solve_time: float = 0.0, plan: List[List[torch.Tensor]] | None = None, interpolated_plan: curobo.types.state.JointState | None = None, metrics: curobo.rollout.rollout_base.RolloutMetrics | None = None, valid_query: bool = True, debug_info: Any | None = None, optimized_dt: torch.Tensor | None = None, path_buffer_last_tstep: List[int] | None = None, )

Bases: object

success: Tensor

start_q: Tensor

goal_q: Tensor

path_length: Tensor | None = None

solve_time: float = 0.0

plan: List[List[Tensor]] | None = None

interpolated_plan: JointState | None = None

metrics: RolloutMetrics | None = None

valid_query: bool = True

debug_info: Any | None = None

optimized_dt: Tensor | None = None

path_buffer_last_tstep: List[int] | None = None

class Graph( nodes: torch.Tensor, edges: torch.Tensor, connectivity: torch.Tensor, shortest_path_lengths: torch.Tensor | None = None, )

Bases: object

nodes: Tensor

edges: Tensor

connectivity: Tensor

shortest_path_lengths: Tensor | None = None

set_shortest_path_lengths( shortest_path_lengths: Tensor, )

get_node_distance()

class GraphConfig( max_nodes: int, steer_delta_buffer: int, sample_pts: int, node_similarity_distance: float, rejection_ratio: int, k_nn: int, c_max: float, vertex_n: int, graph_max_attempts: int, graph_min_attempts: int, init_nodes: int, use_bias_node: bool, dof: int, bounds: torch.Tensor, tensor_args: curobo.types.base.TensorDeviceType, rollout_fn: curobo.rollout.rollout_base.RolloutBase, safety_rollout_fn: curobo.rollout.rollout_base.RolloutBase, max_buffer: int, max_cg_buffer: int, compute_metrics: bool, interpolation_type: curobo.util.trajectory.InterpolateType, interpolation_steps: int, seed: int, use_cuda_graph_mask_samples: bool, distance_weight: torch.Tensor, bias_node: torch.Tensor, interpolation_dt: float = 0.02, interpolation_deviation: float = 0.05, interpolation_acceleration_scale: float = 0.5, )

Bases: object

max_nodes: int

steer_delta_buffer: int

sample_pts: int

node_similarity_distance: float

rejection_ratio: int

k_nn: int

c_max: float

vertex_n: int

graph_max_attempts: int

graph_min_attempts: int

init_nodes: int

use_bias_node: bool

dof: int

bounds: Tensor

tensor_args: TensorDeviceType

rollout_fn: RolloutBase

safety_rollout_fn: RolloutBase

max_buffer: int

max_cg_buffer: int

compute_metrics: bool

interpolation_type: InterpolateType

interpolation_steps: int

seed: int

use_cuda_graph_mask_samples: bool

distance_weight: Tensor

bias_node: Tensor

interpolation_dt: float = 0.02

interpolation_deviation: float = 0.05

interpolation_acceleration_scale: float = 0.5

static from_dict( graph_dict: Dict, tensor_args: TensorDeviceType, rollout_fn: RolloutBase, safety_rollout_fn: RolloutBase, use_cuda_graph: bool = True, )

static load_from_robot_config( robot_cfg: str | Dict | RobotConfig, world_model: str | Dict | 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), world_coll_checker: WorldCollision | None = None, base_cfg_file: str = 'base_cfg.yml', graph_file: str = 'graph.yml', self_collision_check: bool = True, use_cuda_graph: bool = True, seed: int | None = None, )

class GraphPlanBase( config: GraphConfig | None = None, )

Bases: GraphConfig

check_feasibility(x_set)

get_feasible_sample_set( x_samples, )

mask_samples(x_samples)

_mask_samples_cuda_graph( x_samples, )

_mask_samples(x_samples)

_cuda_graph_rollout_constraint( x_samples, use_batch_env=False, )

get_samples( n_samples: int, bounded: bool = True, )

_sample_pts( n_samples=None, bounded=False, unit_ball=False, seed=123, )

reset_buffer()

get_biased_vertex_set( x_start, x_goal, c_max=10.0, c_min=1, n=None, lazy=False, )

_compute_rotation_frame( x_start, x_goal, )

get_new_vertex_set( n=None, lazy=False, )

validate_graph()

get_graph_edges()

Return edges in the graph with start node and end node locations

Returns:: tensor

get_graph()

_validate_graph()

_get_graph_shortest_path( start_node_idx, goal_node_idx, return_length=False, )

batch_get_graph_shortest_path( start_idx_list, goal_idx_list, return_length=False, )

batch_shortcut_path( g_path, start_idx, goal_idx, )

get_node_idx( goal_state, exact=False, ) → int | None

get_path_lengths(goal_idx)

get_graph_shortest_path_lengths( goal_idx: int, )

path_exists( start_node_idx, goal_node_idx, )

batch_path_exists( start_idx_list, goal_idx_list, all_paths=False, )

find_paths( x_init, x_goal, interpolation_steps: int | None = None, ) → GraphResult

abstract _find_paths( x_search, c_search, x_init, ) → GraphResult

compute_path_length(path)

reset_graph()

_distance( pt, batch_pts, norm=True, )

distance( pt, batch_pts, norm=True, )

_hybrid_nearest( sample_node, path, radius, k_n=10, )

_nearest( sample_point, current_graph, )

_k_nearest( sample_point, current_graph, k=10, )

_batch_k_nearest( sample_point, current_graph, k=10, )

_near( sample_point, current_graph, radius, )

_batch_steer_and_connect( start_nodes, goal_nodes, add_steer_pts=-1, lazy=False, add_exact_node=False, )

Connect node from start to goal where both are batched.

Parameters:

start_node ([type]) – [description]
goal_nodes ([type]) – [description]

_batch_steer( start_nodes, desired_nodes, steer_radius=None, add_steer_pts=-1, lazy=False, )

_add_batch_edges_to_graph( new_nodes, start_nodes, lazy=False, add_exact_node=False, )

add_nodes_to_graph( nodes, add_exact_node=False, )

_add_unique_nodes_to_graph( nodes, add_exact_node=False, skip_unique_check=False, )

connect_nodes( x_set=None, connect_mode='knn', debug=False, lazy=False, add_exact_node=False, k_nn=10, edge_set=None, )

get_paths(path_list)

get_interpolated_trajectory( trajectory: List[Tensor], interpolation_steps: int | None = None, )

get_metrics( state: State, )

reset_seed()

reset_cuda_graph()

get_all_rollout_instances() → List[RolloutBase]

warmup( x_start: Tensor | None = None, x_goal: Tensor | None = None, )

static from_dict( graph_dict: Dict, tensor_args: TensorDeviceType, rollout_fn: RolloutBase, safety_rollout_fn: RolloutBase, use_cuda_graph: bool = True, )

interpolation_acceleration_scale: float = 0.5

interpolation_deviation: float = 0.05

interpolation_dt: float = 0.02

static load_from_robot_config( robot_cfg: str | Dict | RobotConfig, world_model: str | Dict | 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), world_coll_checker: WorldCollision | None = None, base_cfg_file: str = 'base_cfg.yml', graph_file: str = 'graph.yml', self_collision_check: bool = True, use_cuda_graph: bool = True, seed: int | None = None, )