curobo.rollout.dynamics_model.kinematic_model module

class TimeTrajConfig( base_dt: float, base_ratio: float, max_dt: float, )

Bases: object

base_dt: float

base_ratio: float

max_dt: float

get_dt_array( num_points: int, )

update_dt( all_dt: float | None = None, base_dt: float | None = None, max_dt: float | None = None, base_ratio: float | None = None, )

class KinematicModelState( state_seq: curobo.types.state.JointState, ee_pos_seq: torch.Tensor | None = None, ee_quat_seq: torch.Tensor | None = None, robot_spheres: torch.Tensor | None = None, link_pos_seq: torch.Tensor | None = None, link_quat_seq: torch.Tensor | None = None, lin_jac_seq: torch.Tensor | None = None, ang_jac_seq: torch.Tensor | None = None, link_names: List[str] | None = None, )

Bases: Sequence

state_seq: JointState

ee_pos_seq: Tensor | None = None

ee_quat_seq: Tensor | None = None

robot_spheres: Tensor | None = None

link_pos_seq: Tensor | None = None

link_quat_seq: Tensor | None = None

lin_jac_seq: Tensor | None = None

ang_jac_seq: Tensor | None = None

link_names: List[str] | None = None

property ee_pose: Pose

property link_pose

_abc_impl = <_abc._abc_data object>

_is_protocol = False

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

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.

class KinematicModelConfig( robot_config: curobo.types.robot.RobotConfig, dt_traj_params: curobo.rollout.dynamics_model.kinematic_model.TimeTrajConfig, tensor_args: curobo.types.base.TensorDeviceType, vel_scale: float = 1.0, state_estimation_variance: float = 0.0, batch_size: int = 1, horizon: int = 5, control_space: curobo.types.enum.StateType = <StateType.ACCELERATION: 2>, state_filter_cfg: Optional[curobo.util.state_filter.FilterConfig] = None, teleport_mode: bool = False, return_full_act_buffer: bool = False, state_finite_difference_mode: str = 'BACKWARD', filter_robot_command: bool = False, )

Bases: object

robot_config: RobotConfig

dt_traj_params: TimeTrajConfig

tensor_args: TensorDeviceType

vel_scale: float = 1.0

state_estimation_variance: float = 0.0

batch_size: int = 1

horizon: int = 5

control_space: StateType = 2

state_filter_cfg: FilterConfig | None = None

teleport_mode: bool = False

return_full_act_buffer: bool = False

state_finite_difference_mode: str = 'BACKWARD'

filter_robot_command: bool = False

static from_dict( data_dict_in, robot_cfg: Dict | RobotConfig, 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), )

class KinematicModel( kinematic_model_config: KinematicModelConfig, )

Bases: KinematicModelConfig

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

get_next_state( curr_state: Tensor, act: Tensor, dt, ): Does a single step from the current state Args: curr_state: current state act: action dt: time to integrate Returns: next_state TODO: Move this into tensorstep class?

tensor_step( state: JointState, act: Tensor, state_seq: JointState, state_idx: Tensor | None = None, ) → JointState: Args: state: [1,N] act: [H,N] todo: Integration with variable dt along trajectory

robot_cmd_tensor_step( state: JointState, act: Tensor, state_seq: JointState, state_idx: Tensor | None = None, ) → JointState: Args: state: [1,N] act: [H,N] todo: Integration with variable dt along trajectory

update_cmd_batch_size( batch_size, )

update_batch_size( batch_size, force_update=False, )

forward( start_state: JointState, act_seq: Tensor, start_state_idx: Tensor | None = None, ) → KinematicModelState

integrate_action(act_seq)

integrate_action_step( act, dt, )

filter_robot_state( current_state: JointState, )

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

property action_bound_lows

property action_bound_highs

property init_action_mean

property retract_config

batch_size: int = 1

control_space: StateType = 2

property cspace_distance_weight

filter_robot_command: bool = False

static from_dict( data_dict_in, robot_cfg: Dict | RobotConfig, 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), )