curobo.rollout.arm_reacher module¶

class ArmReacherMetrics( cost: 'Optional[T_BValue_float]' = None, constraint: 'Optional[T_BValue_float]' = None, feasible: 'Optional[T_BValue_bool]' = None, state: 'Optional[State]' = None, cspace_error: torch.Tensor | None = None, position_error: torch.Tensor | None = None, rotation_error: torch.Tensor | None = None, pose_error: torch.Tensor | None = None, goalset_index: torch.Tensor | None = None, null_space_error: torch.Tensor | None = None, )¶

Bases: RolloutMetrics

cspace_error: Tensor | None = None¶

position_error: Tensor | None = None¶

rotation_error: Tensor | None = None¶

pose_error: Tensor | None = None¶

goalset_index: Tensor | None = None¶

null_space_error: Tensor | None = None¶

clone( clone_state=False, )¶

_abc_impl = <_abc._abc_data object>¶

_is_protocol = False¶

constraint: T_BValue_float | None = None¶

cost: T_BValue_float | None = None¶

count( value, ) → integer -- return number of occurrences of value¶

feasible: T_BValue_bool | None = None¶

index( value[, start[, stop,]] ) → integer -- return first index of value.¶

Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

state: State | None = None¶

Bases: ArmCostConfig

pose_cfg: PoseCostConfig | None = None¶

cspace_cfg: DistCostConfig | None = None¶

straight_line_cfg: CostConfig | None = None¶

zero_acc_cfg: CostConfig | None = None¶

zero_vel_cfg: CostConfig | None = None¶

zero_jerk_cfg: CostConfig | None = None¶

link_pose_cfg: PoseCostConfig | None = None¶

static _get_base_keys()¶

static from_dict( data_dict: Dict, robot_cfg: RobotConfig, world_coll_checker: WorldCollision | 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), )¶

static _get_formatted_dict( data_dict: Dict, cost_key_list: Dict, robot_config: RobotConfig, world_coll_checker: WorldCollision | 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), )¶

bound_cfg: BoundCostConfig | None = None¶

manipulability_cfg: ManipulabilityCostConfig | None = None¶

null_space_cfg: DistCostConfig | None = None¶

primitive_collision_cfg: PrimitiveCollisionCostConfig | None = None¶

self_collision_cfg: SelfCollisionCostConfig | None = None¶

stop_cfg: StopCostConfig | None = None¶

class ArmReacherConfig( tensor_args: 'TensorDeviceType', sum_horizon: 'bool' = False, sampler_seed: 'int' = 1312, model_cfg: curobo.rollout.dynamics_model.kinematic_model.KinematicModelConfig | None = None, cost_cfg: curobo.rollout.arm_reacher.ArmReacherCostConfig = None, constraint_cfg: curobo.rollout.arm_reacher.ArmReacherCostConfig = None, convergence_cfg: curobo.rollout.arm_reacher.ArmReacherCostConfig = None, world_coll_checker: curobo.geom.sdf.world.WorldCollision | None = None, )¶

Bases: ArmBaseConfig

cost_cfg: ArmReacherCostConfig = None¶

constraint_cfg: ArmReacherCostConfig = None¶

convergence_cfg: ArmReacherCostConfig = None¶

static cost_from_dict( cost_data_dict: Dict, robot_cfg: RobotConfig, world_coll_checker: WorldCollision | 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), )¶

classmethod from_dict( robot_cfg: Dict | RobotConfig, model_data_dict: Dict, cost_data_dict: Dict, constraint_data_dict: Dict, convergence_data_dict: Dict, world_coll_checker_dict: Dict | None = None, world_model_dict: Dict | None = None, world_coll_checker: WorldCollision | 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), )¶

Create ArmBase class from dictionary

NOTE: We declare this as a classmethod to allow for derived classes to use it.

Parameters:

robot_cfg (Union[Dict, RobotConfig]) – _description_
model_data_dict (Dict) – _description_
cost_data_dict (Dict) – _description_
constraint_data_dict (Dict) – _description_
convergence_data_dict (Dict) – _description_
world_coll_checker_dict (Optional[Dict], optional) – _description_. Defaults to None.
world_model_dict (Optional[Dict], optional) – _description_. Defaults to None.
world_coll_checker (Optional[WorldCollision], optional) – _description_. Defaults to None.
tensor_args (TensorDeviceType, optional) – _description_. Defaults to TensorDeviceType().

Returns:

_description_

Return type:

_type_

model_cfg: KinematicModelConfig | None = None¶

static model_from_dict( model_data_dict: Dict, robot_cfg: RobotConfig, 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), )¶

sampler_seed: int = 1312¶

sum_horizon: bool = False¶

world_coll_checker: WorldCollision | None = None¶

static world_coll_checker_from_dict( world_coll_checker_dict: Dict | None = None, world_model_dict: WorldConfig | Dict | None = None, world_coll_checker: WorldCollision | 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), )¶

tensor_args: TensorDeviceType¶

_compute_g_dist_jit( rot_err_norm, goal_dist, )¶

class ArmReacher( config: ArmReacherConfig | None = None, )¶

Bases: ArmBase, ArmReacherConfig

cost_fn( state: KinematicModelState, action_batch=None, )¶

Compute cost given that state dictionary and actions

curobo.rollout.cost.PoseCost curobo.rollout.cost.DistCost

convergence_fn( state: KinematicModelState, out_metrics: ArmReacherMetrics | None = None, ) → ArmReacherMetrics¶

update_params( goal: Goal, )¶: Update params for the cost terms and dynamics model.

enable_pose_cost( enable: bool = True, )¶

_get_augmented_state( state: JointState, ) → KinematicModelState¶

_init_after_config_load()¶

property action_bound_highs¶

property action_bound_lows¶

abstract property action_bounds¶

property action_horizon¶

break_cuda_graph()¶

compute_kinematics( state: JointState, ) → KinematicModelState¶

constraint_cfg: ArmReacherCostConfig = None¶

constraint_fn( state: KinematicModelState, out_metrics: RolloutMetrics | None = None, use_batch_env: bool = True, ) → RolloutMetrics¶

convergence_cfg: ArmReacherCostConfig = None¶

cost_cfg: ArmReacherCostConfig = None¶

static cost_from_dict( cost_data_dict: Dict, robot_cfg: RobotConfig, world_coll_checker: WorldCollision | 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), )¶

property cspace_config: CSpaceConfig¶

property cuda_graph_instance¶

current_cost(

current_state: JointState,

no_coll=False,

return_state=True,

**kwargs,

)¶

property d_action¶

property dt¶

enable_cspace_cost( enable: bool = True, )¶

filter_robot_state( current_state: JointState, ) → JointState¶

Create ArmBase class from dictionary

NOTE: We declare this as a classmethod to allow for derived classes to use it.

Parameters:

robot_cfg (Union[Dict, RobotConfig]) – _description_
model_data_dict (Dict) – _description_
cost_data_dict (Dict) – _description_
constraint_data_dict (Dict) – _description_
convergence_data_dict (Dict) – _description_
world_coll_checker_dict (Optional[Dict], optional) – _description_. Defaults to None.
world_model_dict (Optional[Dict], optional) – _description_. Defaults to None.
world_coll_checker (Optional[WorldCollision], optional) – _description_. Defaults to None.
tensor_args (TensorDeviceType, optional) – _description_. Defaults to TensorDeviceType().

Returns:

_description_

Return type:

_type_

get_action_from_state( state: JointState, )¶

get_ee_pose(current_state)¶

get_full_dof_from_solution( q_js: JointState, ) → JointState¶

This function will all the dof that are locked during optimization.

Parameters:: q_sol – _description_
Returns:: _description_

get_init_action_seq() → Tensor¶

get_metrics( state: JointState | KinematicModelState, )¶

Compute metrics given state

Parameters:: state (Union[JointState, URDFModelState]) – _description_
Returns:: _description_
Return type:: _type_

get_metrics_cuda_graph( state: JointState, )¶

Use a CUDA Graph to compute metrics

Parameters:: state – _description_
Raises:: ValueError – _description_
Returns:: _description_

get_robot_command( current_state: JointState, act_seq: Tensor, shift_steps: int = 1, state_idx: Tensor | None = None, ) → JointState¶

get_state_from_action( start_state: JointState, act_seq: Tensor, state_idx: Tensor | None = None, )¶

property horizon¶

property joint_names: List[str]¶

property kinematics¶

model_cfg: KinematicModelConfig | None = None¶

static model_from_dict( model_data_dict: Dict, robot_cfg: RobotConfig, 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), )¶

reset()¶

reset_cuda_graph()¶

reset_seed()¶

reset_shape()¶

property retract_state¶

rollout_constraint( act_seq: Tensor, use_batch_env: bool = True, ) → RolloutMetrics¶

rollout_constraint_cuda_graph( act_seq: Tensor, use_batch_env: bool = True, )¶

rollout_fn( act_seq, ) → Trajectory¶

Return sequence of costs and states encountered by simulating a batch of action sequences

Parameters:: action_seq (torch.Tensor [num_particles, horizon, d_act])

sample_random_actions( n: int = 0, )¶

sampler_seed: int = 1312¶

property state_bounds: Dict[str, List[float]]¶

sum_horizon: bool = False¶

update_cost_dt(dt: float)¶

update_start_state( start_state: Tensor, )¶

update_traj_dt( dt: float | Tensor, base_dt: float | None = None, max_dt: float | None = None, base_ratio: float | None = None, )¶

world_coll_checker: WorldCollision | None = None¶

static world_coll_checker_from_dict( world_coll_checker_dict: Dict | None = None, world_model_dict: WorldConfig | Dict | None = None, world_coll_checker: WorldCollision | 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), )¶

tensor_args: TensorDeviceType¶

get_pose_costs( include_link_pose: bool = False, include_convergence: bool = True, only_convergence: bool = False, )¶

update_pose_cost_metric( metric: PoseCostMetric, )¶

cat_sum_reacher( tensor_list: List[Tensor], )¶

cat_sum_horizon_reacher( tensor_list: List[Tensor], )¶