Moves delay actuator functionality to IdealPDActuator

source/isaaclab/isaaclab/actuators/actuator_cfg.py

@@ -191,6 +191,12 @@ class IdealPDActuatorCfg(ActuatorBaseCfg):
 
     class_type: type = actuator_pd.IdealPDActuator
 
+    min_delay: int = 0
+    """Minimum number of physics time-steps with which the actuator command may be delayed. Defaults to 0."""
+
+    max_delay: int = 0
+    """Maximum number of physics time-steps with which the actuator command may be delayed. Defaults to 0."""
+
 
 @configclass
 class DCMotorCfg(IdealPDActuatorCfg):
@@ -258,15 +264,16 @@ class DelayedPDActuatorCfg(IdealPDActuatorCfg):
 
     class_type: type = actuator_pd.DelayedPDActuator
 
-    min_delay: int = 0
-    """Minimum number of physics time-steps with which the actuator command may be delayed. Defaults to 0."""
-
-    max_delay: int = 0
-    """Maximum number of physics time-steps with which the actuator command may be delayed. Defaults to 0."""
+    def __post_init__(self):
+        print(
+            DeprecationWarning(
+                "DelayPDActuatorCfg has been deprecated. All functionality has been added to IdealPDActuatorCfg"
+            )
+        )
 
 
 @configclass
-class RemotizedPDActuatorCfg(DelayedPDActuatorCfg):
+class RemotizedPDActuatorCfg(IdealPDActuatorCfg):
     """Configuration for a remotized PD actuator.
 
     Note:

source/isaaclab/isaaclab/actuators/actuator_pd.py

@@ -169,6 +169,15 @@ class IdealPDActuator(ActuatorBase):
     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.
+
+    An optional functionality of 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 delayed by a certain number of physics steps.
+
+    The amount of time delay is configurable and can be set to a random value between the minimum and maximum time
+    delay bounds at every reset. The minimum and maximum time delay values are set in the configuration instance passed
+    to the class.
+
     """
 
     cfg: IdealPDActuatorCfg
@@ -178,12 +187,51 @@ class IdealPDActuator(ActuatorBase):
     Operations.
     """
 
+    def __init__(self, cfg: IdealPDActuatorCfg, *args, **kwargs):
+        super().__init__(cfg, *args, **kwargs)
+        if cfg.max_delay < cfg.min_delay:
+            raise ValueError("IdealPDActuatorCfg: max_delay must be greater than or equal to min_delay")
+        # instantiate the delay buffers
+        if cfg.max_delay > 0:
+            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)
+
     def reset(self, env_ids: Sequence[int]):
-        pass
+        # 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
+        if self.cfg.max_delay > 0:
+            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)
 
     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 set points
+        if self.cfg.max_delay > 0:
+            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)
+
         # compute errors
         error_pos = control_action.joint_positions - joint_pos
         error_vel = control_action.joint_velocities - joint_vel
@@ -307,67 +355,26 @@ def _clip_effort(self, effort: torch.Tensor) -> torch.Tensor:
 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.
+    .. deprecated:: 2.2.0
+        Please use :class:`IdealPDActuator` instead.
     """
 
     cfg: DelayedPDActuatorCfg
     """The configuration for the actuator model."""
 
     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)
-
-    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,
+        print(
+            DeprecationWarning(
+                "DelayPDActuator has been deprecated. All functionality has been added to IdealPDActuator"
+            )
         )
-        # 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)
-
-    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)
 
 
-class RemotizedPDActuator(DelayedPDActuator):
+class RemotizedPDActuator(IdealPDActuator):
     """Ideal PD actuator with angle-dependent torque limits.
 
-    This class extends the :class:`DelayedPDActuator` class by adding angle-dependent torque limits to the actuator.
+    This class extends the :class:`IdealPDActuator` 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.
 

source/isaaclab/test/actuators/test_ideal_pd_actuator.py

@@ -16,7 +16,8 @@
 
 import pytest
 
-from isaaclab.actuators import IdealPDActuatorCfg
+from isaaclab.actuators import IdealPDActuator, IdealPDActuatorCfg
+from isaaclab.utils.buffers import DelayBuffer
 from isaaclab.utils.types import ArticulationActions
 
 
@@ -270,5 +271,160 @@ def test_ideal_pd_compute(num_envs, num_joints, device, effort_lim):
     )
 
 
+@pytest.mark.parametrize("num_envs", [1, 2])
+@pytest.mark.parametrize("num_joints", [1, 2])
+@pytest.mark.parametrize("device", ["cuda:0", "cpu"])
+def test_ideal_pd_actuator_init_delay(num_envs, num_joints, device):
+    """Test initialization of ideal pd actuator with delay."""
+    joint_names = [f"joint_{d}" for d in range(num_joints)]
+    joint_ids = [d for d in range(num_joints)]
+    stiffness = 200
+    damping = 10
+    effort_limit = 60.0
+    velocity_limit = 50
+    delay = 5
+
+    actuator_cfg = IdealPDActuatorCfg(
+        joint_names_expr=joint_names,
+        stiffness=stiffness,
+        damping=damping,
+        effort_limit=effort_limit,
+        velocity_limit=velocity_limit,
+        min_delay=delay,
+        max_delay=delay,
+    )
+
+    # assume Articulation class:
+    #   - finds joints (names and ids) associate with the provided joint_names_expr
+
+    actuator = actuator_cfg.class_type(
+        actuator_cfg,
+        joint_names=joint_names,
+        joint_ids=joint_ids,
+        num_envs=num_envs,
+        device=device,
+    )
+    assert isinstance(actuator, IdealPDActuator)
+    # check device and shape
+    torch.testing.assert_close(actuator.computed_effort, torch.zeros(num_envs, num_joints, device=device))
+    torch.testing.assert_close(actuator.applied_effort, torch.zeros(num_envs, num_joints, device=device))
+    torch.testing.assert_close(
+        actuator.effort_limit,
+        effort_limit * torch.ones(num_envs, num_joints, device=device),
+    )
+    torch.testing.assert_close(
+        actuator.velocity_limit, velocity_limit * torch.ones(num_envs, num_joints, device=device)
+    )
+
+    # check delay buffers
+    assert isinstance(actuator.positions_delay_buffer, DelayBuffer)
+    assert isinstance(actuator.velocities_delay_buffer, DelayBuffer)
+    assert isinstance(actuator.efforts_delay_buffer, DelayBuffer)
+
+    assert actuator.positions_delay_buffer.history_length == delay
+    assert actuator.velocities_delay_buffer.history_length == delay
+    assert actuator.efforts_delay_buffer.history_length == delay
+
+
+@pytest.mark.parametrize("num_envs", [1, 2])
+@pytest.mark.parametrize("num_joints", [1, 2])
+@pytest.mark.parametrize("device", ["cuda:0", "cpu"])
+@pytest.mark.parametrize("effort_lim", [None, 80])
+def test_delay_pd_actuator_compute(num_envs, num_joints, device, effort_lim):
+    """Test the computation of the delay pd actuator."""
+    joint_names = [f"joint_{d}" for d in range(num_joints)]
+    joint_ids = [d for d in range(num_joints)]
+    stiffness = 20
+    damping = 1
+    effort_limit = effort_lim
+    velocity_limit = 50
+    delay = 3
+    # configure actuator
+    actuator_cfg = IdealPDActuatorCfg(
+        joint_names_expr=joint_names,
+        stiffness=stiffness,
+        damping=damping,
+        effort_limit=effort_limit,
+        velocity_limit=velocity_limit,
+        min_delay=delay,
+        max_delay=delay,
+    )
+    # instantiate actuator
+    actuator = actuator_cfg.class_type(
+        actuator_cfg,
+        joint_names=joint_names,
+        joint_ids=joint_ids,
+        num_envs=num_envs,
+        device=device,
+        stiffness=actuator_cfg.stiffness,
+        damping=actuator_cfg.damping,
+    )
+    # requires a restart to create the delay
+    actuator.reset(range(num_envs))
+
+    desired_pos = [2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0]
+    desired_vel = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
+    feedforward_effort = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]
+
+    measured_joint_pos = 1.0
+    measured_joint_vel = 0.1
+
+    # check delay buffer filling properly
+    for i in range(delay + 4):
+
+        desired_control_action = ArticulationActions()
+        desired_control_action.joint_positions = desired_pos[i] * torch.ones(num_envs, num_joints, device=device)
+        desired_control_action.joint_velocities = desired_vel[i] * torch.ones(num_envs, num_joints, device=device)
+        desired_control_action.joint_efforts = feedforward_effort[i] * torch.ones(num_envs, num_joints, device=device)
+
+        computed_control_action = actuator.compute(
+            desired_control_action,
+            measured_joint_pos * torch.ones(num_envs, num_joints, device=device),
+            measured_joint_vel * torch.ones(num_envs, num_joints, device=device),
+        )
+
+        if i <= delay:
+            expect = (
+                stiffness * (desired_pos[0] - measured_joint_pos)
+                + damping * (desired_vel[0] - measured_joint_vel)
+                + feedforward_effort[0]
+            )
+        else:
+            expect = (
+                stiffness * (desired_pos[i - delay] - measured_joint_pos)
+                + damping * (desired_vel[i - delay] - measured_joint_vel)
+                + feedforward_effort[i - delay]
+            )
+
+        if effort_lim is not None:
+            expect_apply = min(expect, effort_lim)
+        else:
+            expect_apply = expect
+
+        torch.testing.assert_close(
+            expect * torch.ones(num_envs, num_joints, device=device),
+            actuator.computed_effort,
+        )
+        torch.testing.assert_close(
+            expect_apply * torch.ones(num_envs, num_joints, device=device),
+            actuator.applied_effort,
+        )
+        torch.testing.assert_close(
+            actuator.applied_effort,
+            computed_control_action.joint_efforts,
+        )
+    # check reset single env
+    actuator.reset([0])
+    assert actuator.positions_delay_buffer._circular_buffer._num_pushes[0] == 0
+    assert actuator.velocities_delay_buffer._circular_buffer._num_pushes[0] == 0
+    assert actuator.efforts_delay_buffer._circular_buffer._num_pushes[0] == 0
+    if num_envs > 1:
+        assert actuator.positions_delay_buffer._circular_buffer._num_pushes[1] == i + 1
+        assert actuator.velocities_delay_buffer._circular_buffer._num_pushes[1] == i + 1
+        assert actuator.efforts_delay_buffer._circular_buffer._num_pushes[1] == i + 1
+    # check actuator reset all
+    actuator.reset(range(num_envs))
+
+
 if __name__ == "__main__":
     pytest.main([__file__, "-v", "--maxfail=1"])

source/isaaclab_assets/isaaclab_assets/robots/spot.py

@@ -12,7 +12,7 @@
 """
 
 import isaaclab.sim as sim_utils
-from isaaclab.actuators import DelayedPDActuatorCfg, RemotizedPDActuatorCfg
+from isaaclab.actuators import IdealPDActuatorCfg, RemotizedPDActuatorCfg
 from isaaclab.assets.articulation import ArticulationCfg
 from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR
 
@@ -160,7 +160,7 @@
         joint_vel={".*": 0.0},
     ),
     actuators={
-        "spot_hip": DelayedPDActuatorCfg(
+        "spot_hip": IdealPDActuatorCfg(
             joint_names_expr=[".*_h[xy]"],
             effort_limit=45.0,
             stiffness=60.0,