# Copyright (c) 2022-2024, The Isaac Lab Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from __future__ import annotations
import torch
from collections.abc import Sequence
from typing import TYPE_CHECKING
from omni.isaac.lab.utils import DelayBuffer, LinearInterpolation
from omni.isaac.lab.utils.types import ArticulationActions
from .actuator_base import ActuatorBase
if TYPE_CHECKING:
from .actuator_cfg import (
DCMotorCfg,
DelayedPDActuatorCfg,
IdealPDActuatorCfg,
ImplicitActuatorCfg,
RemotizedPDActuatorCfg,
)
"""
Implicit Actuator Models.
"""
[文档]class ImplicitActuator(ActuatorBase):
"""Implicit actuator model that is handled by the simulation.
This performs a similar function as the :class:`IdealPDActuator` class. However, the PD control is handled
implicitly by the simulation which performs continuous-time integration of the PD control law. This is
generally more accurate than the explicit PD control law used in :class:`IdealPDActuator` when the simulation
time-step is large.
.. note::
The articulation class sets the stiffness and damping parameters from the configuration into the simulation.
Thus, the parameters are not used in this class.
.. caution::
The class is only provided for consistency with the other actuator models. It does not implement any
functionality and should not be used. All values should be set to the simulation directly.
"""
cfg: ImplicitActuatorCfg
"""The configuration for the actuator model."""
"""
Operations.
"""
[文档] def reset(self, *args, **kwargs):
# This is a no-op. There is no state to reset for implicit actuators.
pass
[文档] def compute(
self, control_action: ArticulationActions, joint_pos: torch.Tensor, joint_vel: torch.Tensor
) -> ArticulationActions:
"""Process the actuator group actions and compute the articulation actions.
In case of implicit actuator, the control action is directly returned as the computed action.
This function is a no-op and does not perform any computation on the input control action.
However, it computes the approximate torques for the actuated joint since PhysX does not compute
this quantity explicitly.
Args:
control_action: The joint action instance comprising of the desired joint positions, joint velocities
and (feed-forward) joint efforts.
joint_pos: The current joint positions of the joints in the group. Shape is (num_envs, num_joints).
joint_vel: The current joint velocities of the joints in the group. Shape is (num_envs, num_joints).
Returns:
The computed desired joint positions, joint velocities and joint efforts.
"""
# store approximate torques for reward computation
error_pos = control_action.joint_positions - joint_pos
error_vel = control_action.joint_velocities - joint_vel
self.computed_effort = self.stiffness * error_pos + self.damping * error_vel + control_action.joint_efforts
# clip the torques based on the motor limits
self.applied_effort = self._clip_effort(self.computed_effort)
return control_action
"""
Explicit Actuator Models.
"""
[文档]class IdealPDActuator(ActuatorBase):
r"""Ideal torque-controlled actuator model with a simple saturation model.
It employs the following model for computing torques for the actuated joint :math:`j`:
.. math::
\tau_{j, computed} = k_p * (q - q_{des}) + k_d * (\dot{q} - \dot{q}_{des}) + \tau_{ff}
where, :math:`k_p` and :math:`k_d` are joint stiffness and damping gains, :math:`q` and :math:`\dot{q}`
are the current joint positions and velocities, :math:`q_{des}`, :math:`\dot{q}_{des}` and :math:`\tau_{ff}`
are the desired joint positions, velocities and torques commands.
The clipping model is based on the maximum torque applied by the motor. It is implemented as:
.. math::
\tau_{j, max} & = \gamma \times \tau_{motor, max} \\
\tau_{j, applied} & = clip(\tau_{computed}, -\tau_{j, max}, \tau_{j, max})
where the clipping function is defined as :math:`clip(x, x_{min}, x_{max}) = min(max(x, x_{min}), x_{max})`.
The parameters :math:`\gamma` is the gear ratio of the gear box connecting the motor and the actuated joint ends,
and :math:`\tau_{motor, max}` is the maximum motor effort possible. These parameters are read from
the configuration instance passed to the class.
"""
cfg: IdealPDActuatorCfg
"""The configuration for the actuator model."""
"""
Operations.
"""
[文档] def reset(self, env_ids: Sequence[int]):
pass
[文档] def compute(
self, control_action: ArticulationActions, joint_pos: torch.Tensor, joint_vel: torch.Tensor
) -> ArticulationActions:
# compute errors
error_pos = control_action.joint_positions - joint_pos
error_vel = control_action.joint_velocities - joint_vel
# calculate the desired joint torques
self.computed_effort = self.stiffness * error_pos + self.damping * error_vel + control_action.joint_efforts
# clip the torques based on the motor limits
self.applied_effort = self._clip_effort(self.computed_effort)
# set the computed actions back into the control action
control_action.joint_efforts = self.applied_effort
control_action.joint_positions = None
control_action.joint_velocities = None
return control_action
[文档]class DCMotor(IdealPDActuator):
r"""Direct control (DC) motor actuator model with velocity-based saturation model.
It uses the same model as the :class:`IdealActuator` for computing the torques from input commands.
However, it implements a saturation model defined by DC motor characteristics.
A DC motor is a type of electric motor that is powered by direct current electricity. In most cases,
the motor is connected to a constant source of voltage supply, and the current is controlled by a rheostat.
Depending on various design factors such as windings and materials, the motor can draw a limited maximum power
from the electronic source, which limits the produced motor torque and speed.
A DC motor characteristics are defined by the following parameters:
* Continuous-rated speed (:math:`\dot{q}_{motor, max}`) : The maximum-rated speed of the motor.
* Continuous-stall torque (:math:`\tau_{motor, max}`): The maximum-rated torque produced at 0 speed.
* Saturation torque (:math:`\tau_{motor, sat}`): The maximum torque that can be outputted for a short period.
Based on these parameters, the instantaneous minimum and maximum torques are defined as follows:
.. math::
\tau_{j, max}(\dot{q}) & = clip \left (\tau_{j, sat} \times \left(1 -
\frac{\dot{q}}{\dot{q}_{j, max}}\right), 0.0, \tau_{j, max} \right) \\
\tau_{j, min}(\dot{q}) & = clip \left (\tau_{j, sat} \times \left( -1 -
\frac{\dot{q}}{\dot{q}_{j, max}}\right), - \tau_{j, max}, 0.0 \right)
where :math:`\gamma` is the gear ratio of the gear box connecting the motor and the actuated joint ends,
:math:`\dot{q}_{j, max} = \gamma^{-1} \times \dot{q}_{motor, max}`, :math:`\tau_{j, max} =
\gamma \times \tau_{motor, max}` and :math:`\tau_{j, peak} = \gamma \times \tau_{motor, peak}`
are the maximum joint velocity, maximum joint torque and peak torque, respectively. These parameters
are read from the configuration instance passed to the class.
Using these values, the computed torques are clipped to the minimum and maximum values based on the
instantaneous joint velocity:
.. math::
\tau_{j, applied} = clip(\tau_{computed}, \tau_{j, min}(\dot{q}), \tau_{j, max}(\dot{q}))
"""
cfg: DCMotorCfg
"""The configuration for the actuator model."""
[文档] def __init__(self, cfg: DCMotorCfg, *args, **kwargs):
super().__init__(cfg, *args, **kwargs)
# parse configuration
if self.cfg.saturation_effort is not None:
self._saturation_effort = self.cfg.saturation_effort
else:
self._saturation_effort = torch.inf
# prepare joint vel buffer for max effort computation
self._joint_vel = torch.zeros_like(self.computed_effort)
# create buffer for zeros effort
self._zeros_effort = torch.zeros_like(self.computed_effort)
# check that quantities are provided
if self.cfg.velocity_limit is None:
raise ValueError("The velocity limit must be provided for the DC motor actuator model.")
"""
Operations.
"""
[文档] def compute(
self, control_action: ArticulationActions, joint_pos: torch.Tensor, joint_vel: torch.Tensor
) -> ArticulationActions:
# save current joint vel
self._joint_vel[:] = joint_vel
# calculate the desired joint torques
return super().compute(control_action, joint_pos, joint_vel)
"""
Helper functions.
"""
def _clip_effort(self, effort: torch.Tensor) -> torch.Tensor:
# compute torque limits
# -- max limit
max_effort = self._saturation_effort * (1.0 - self._joint_vel / self.velocity_limit)
max_effort = torch.clip(max_effort, min=self._zeros_effort, max=self.effort_limit)
# -- min limit
min_effort = self._saturation_effort * (-1.0 - self._joint_vel / self.velocity_limit)
min_effort = torch.clip(min_effort, min=-self.effort_limit, max=self._zeros_effort)
# clip the torques based on the motor limits
return torch.clip(effort, min=min_effort, max=max_effort)
[文档]class DelayedPDActuator(IdealPDActuator):
"""Ideal PD actuator with delayed command application.
This class extends the :class:`IdealPDActuator` class by adding a delay to the actuator commands. The delay
is implemented using a circular buffer that stores the actuator commands for a certain number of physics steps.
The most recent actuation value is pushed to the buffer at every physics step, but the final actuation value
applied to the simulation is lagged by a certain number of physics steps.
The amount of time lag is configurable and can be set to a random value between the minimum and maximum time
lag bounds at every reset. The minimum and maximum time lag values are set in the configuration instance passed
to the class.
"""
cfg: DelayedPDActuatorCfg
"""The configuration for the actuator model."""
[文档] def __init__(self, cfg: DelayedPDActuatorCfg, *args, **kwargs):
super().__init__(cfg, *args, **kwargs)
# instantiate the delay buffers
self.positions_delay_buffer = DelayBuffer(cfg.max_delay, self._num_envs, device=self._device)
self.velocities_delay_buffer = DelayBuffer(cfg.max_delay, self._num_envs, device=self._device)
self.efforts_delay_buffer = DelayBuffer(cfg.max_delay, self._num_envs, device=self._device)
# all of the envs
self._ALL_INDICES = torch.arange(self._num_envs, dtype=torch.long, device=self._device)
[文档] def reset(self, env_ids: Sequence[int]):
super().reset(env_ids)
# number of environments (since env_ids can be a slice)
if env_ids is None or env_ids == slice(None):
num_envs = self._num_envs
else:
num_envs = len(env_ids)
# set a new random delay for environments in env_ids
time_lags = torch.randint(
low=self.cfg.min_delay,
high=self.cfg.max_delay + 1,
size=(num_envs,),
dtype=torch.int,
device=self._device,
)
# set delays
self.positions_delay_buffer.set_time_lag(time_lags, env_ids)
self.velocities_delay_buffer.set_time_lag(time_lags, env_ids)
self.efforts_delay_buffer.set_time_lag(time_lags, env_ids)
# reset buffers
self.positions_delay_buffer.reset(env_ids)
self.velocities_delay_buffer.reset(env_ids)
self.efforts_delay_buffer.reset(env_ids)
[文档] def compute(
self, control_action: ArticulationActions, joint_pos: torch.Tensor, joint_vel: torch.Tensor
) -> ArticulationActions:
# apply delay based on the delay the model for all the setpoints
control_action.joint_positions = self.positions_delay_buffer.compute(control_action.joint_positions)
control_action.joint_velocities = self.velocities_delay_buffer.compute(control_action.joint_velocities)
control_action.joint_efforts = self.efforts_delay_buffer.compute(control_action.joint_efforts)
# compte actuator model
return super().compute(control_action, joint_pos, joint_vel)
[文档]class RemotizedPDActuator(DelayedPDActuator):
"""Ideal PD actuator with angle-dependent torque limits.
This class extends the :class:`DelayedPDActuator` class by adding angle-dependent torque limits to the actuator.
The torque limits are applied by querying a lookup table describing the relationship between the joint angle
and the maximum output torque. The lookup table is provided in the configuration instance passed to the class.
The torque limits are interpolated based on the current joint positions and applied to the actuator commands.
"""
[文档] def __init__(
self,
cfg: RemotizedPDActuatorCfg,
joint_names: list[str],
joint_ids: Sequence[int],
num_envs: int,
device: str,
stiffness: torch.Tensor | float = 0.0,
damping: torch.Tensor | float = 0.0,
armature: torch.Tensor | float = 0.0,
friction: torch.Tensor | float = 0.0,
effort_limit: torch.Tensor | float = torch.inf,
velocity_limit: torch.Tensor | float = torch.inf,
):
# remove effort and velocity box constraints from the base class
cfg.effort_limit = torch.inf
cfg.velocity_limit = torch.inf
# call the base method and set default effort_limit and velocity_limit to inf
super().__init__(
cfg, joint_names, joint_ids, num_envs, device, stiffness, damping, armature, friction, torch.inf, torch.inf
)
self._joint_parameter_lookup = cfg.joint_parameter_lookup.to(device=device)
# define remotized joint torque limit
self._torque_limit = LinearInterpolation(self.angle_samples, self.max_torque_samples, device=device)
"""
Properties.
"""
@property
def angle_samples(self) -> torch.Tensor:
return self._joint_parameter_lookup[:, 0]
@property
def transmission_ratio_samples(self) -> torch.Tensor:
return self._joint_parameter_lookup[:, 1]
@property
def max_torque_samples(self) -> torch.Tensor:
return self._joint_parameter_lookup[:, 2]
"""
Operations.
"""
[文档] def compute(
self, control_action: ArticulationActions, joint_pos: torch.Tensor, joint_vel: torch.Tensor
) -> ArticulationActions:
# call the base method
control_action = super().compute(control_action, joint_pos, joint_vel)
# compute the absolute torque limits for the current joint positions
abs_torque_limits = self._torque_limit.compute(joint_pos)
# apply the limits
control_action.joint_efforts = torch.clamp(
control_action.joint_efforts, min=-abs_torque_limits, max=abs_torque_limits
)
self.applied_effort = control_action.joint_efforts
return control_action
