curobo.rollout.rollout_base module
- class RolloutMetrics(
- cost: 'Optional[T_BValue_float]' = None,
- constraint: 'Optional[T_BValue_float]' = None,
- feasible: 'Optional[T_BValue_bool]' = None,
- state: 'Optional[State]' = None,
Bases:
Sequence
- clone(clone_state=False)
- _abc_impl = <_abc._abc_data object>
- _is_protocol = False
- count(
- value,
- index(
- value[,
- start[,
- stop,]]
Raises ValueError if the value is not present.
Supporting start and stop arguments is optional, but recommended.
- class Trajectory(
- actions: 'T_BHDOF_float',
- costs: 'T_BHValue_float',
- state: 'Optional[State]' = None,
- debug: 'Optional[dict]' = None,
Bases:
object
- class Goal(
- name: str = 'goal',
- goal_state: State | None = None,
- goal_pose: Pose = Pose(position=None, quaternion=None, rotation=None, batch=1, n_goalset=1, name='ee_link', normalize_rotation=True),
- links_goal_pose: Dict[str, Pose] | None = None,
- current_state: State | None = None,
- retract_state: Tensor | None = None,
- batch: int = -1,
- batch_pose_idx: Tensor | None = None,
- batch_goal_state_idx: Tensor | None = None,
- batch_retract_state_idx: Tensor | None = None,
- batch_current_state_idx: Tensor | None = None,
- batch_enable_idx: Tensor | None = None,
- batch_world_idx: Tensor | None = None,
- update_batch_idx_buffers: bool = True,
- n_goalset: int = 1,
Bases:
Sequence
Goal data class used to update optimization target.
#NOTE: We can parallelize Goal in two ways: 1. Solve for current_state, pose pair in same environment 2. Solve for current_state, pose pair in different environment For case (1), we use batch_pose_idx to find the memory address of the current_state, pose pair while keeping batch_world_idx = [0] For case (2), we add a batch_world_idx[0,1,2..].
- goal_pose: Pose = Pose(position=None, quaternion=None, rotation=None, batch=1, n_goalset=1, name='ee_link', normalize_rotation=True)
- _update_batch_size()
- clone()
- _tensor_repeat_seeds(
- tensor,
- num_seeds,
- apply_kernel(kernel_mat)
- to(
- tensor_args: TensorDeviceType,
- copy_( )
Copy data from another goal object.
- Parameters:
goal (Goal) – _description_
- Raises:
NotImplementedError – _description_
NotImplementedError – _description_
- Returns:
_description_
- Return type:
_type_
- _copy_buffer(ref_buffer, buffer)
- _copy_tensor(ref_buffer, buffer)
- get_batch_goal_state()
- create_index_buffers(
- batch_size: int,
- batch_env: bool,
- batch_retract: bool,
- num_seeds: int,
- tensor_args: TensorDeviceType,
- classmethod create_idx(
- pose_batch_size: int,
- batch_env: bool,
- batch_retract: bool,
- num_seeds: int,
- tensor_args: TensorDeviceType,
- _abc_impl = <_abc._abc_data object>
- _is_protocol = False
- count(
- value,
- index(
- value[,
- start[,
- stop,]]
Raises ValueError if the value is not present.
Supporting start and stop arguments is optional, but recommended.
- class RolloutConfig(
- tensor_args: 'TensorDeviceType',
- sum_horizon: 'bool' = False,
- sampler_seed: 'int' = 1312,
Bases:
object
- tensor_args: TensorDeviceType
- class RolloutBase(
- config: RolloutConfig | None = None,
Bases:
object
- _init_after_config_load()
- abstract constraint_fn(
- state: State,
- out_metrics: RolloutMetrics | None = None,
- abstract convergence_fn(
- state: State,
- out_metrics: RolloutMetrics | None = None,
- rollout_fn(act)
- current_cost(current_state)
- abstract property action_bounds
- abstract get_robot_command( )
- reset_seed()
- reset()
- abstract property action_bound_lows
- abstract property action_bound_highs
- abstract property dt
- abstract get_init_action_seq()
- abstract rollout_constraint(
- act_seq: Tensor,
- reset_cuda_graph()
- reset_shape()
- abstract get_state_from_action( )
- abstract property cspace_config: CSpaceConfig
- get_full_dof_from_solution(
- q_js: JointState,