respond to reviewer comments

Author: bmccann-bdai

source/isaaclab/isaaclab/actuators/actuator_base.py

@@ -76,9 +76,15 @@ class ActuatorBase(ABC):
     """
 
     drive_model: torch.Tensor
-    """Three parameters for each joint/env defining the speed_effort_gradient, maximum actuator velocity, and
-    velocity_dependent_resistance which define velocity and effort dependent constraints on the motor's performance.
-    This feature is only implemented in IsaacSim v5.0. Shape is (num_envs, num_joints, 3)."""
+    """Three parameters for each joint/env defining the:
+       (1)  [:,:,0] speed_effort_gradient : float = 1 (default),
+       (2)  [:,:,1] maximum_actuator_velocity : float = torch.inf (default), and
+       (3)  [:,:,2] velocity_dependent_resistance : float = 1 (default)
+       which define velocity and effort dependent constraints on the motor's performance.
+
+    This feature is only implemented in IsaacSim v5.0.
+
+    The shape is (num_envs, num_joints, 3)."""
 
     stiffness: torch.Tensor
     """The stiffness (P gain) of the PD controller. Shape is (num_envs, num_joints)."""
@@ -369,14 +375,13 @@ def _parse_joint_parameter(
                         + f"actuator on joints {self.joint_names}. Expected float or dict, got tuple"
                     )
                 # ensure the tuple is the correct length, and assign to the last tensor dimensions across all joints
-                if len(cfg_value) is expected_shape[2]:
-                    for i, v in enumerate(cfg_value):
-                        param[:, :, i] = float(v)
-                else:
+                if not len(cfg_value) is expected_shape[2]:
                     raise ValueError(
                         f"Invalid tuple length for parameter {param_name}, got {len(cfg_value)}, expected"
                         + f" {expected_shape[2]}"
                     )
+                for i, v in enumerate(cfg_value):
+                    param[:, :, i] = float(v)
             elif isinstance(cfg_value, dict):
                 # if dict, then parse the regular expression
                 indices, _, values = string_utils.resolve_matching_names_values(cfg_value, self.joint_names)
@@ -415,14 +420,13 @@ def _parse_joint_parameter(
                         + f"actuator on joints {self.joint_names}. Expected float or dict, got tuple"
                     )
                 # ensure the tuple is the correct length, and assign to the last tensor dimensions across all joints
-                if len(default_value) is expected_shape[2]:
-                    for i, v in enumerate(default_value):
-                        param[:, :, i] = float(v)
-                else:
+                if not len(default_value) is expected_shape[2]:
                     raise ValueError(
                         f"Invalid tuple length for parameter {param_name}, got {len(default_value)}, expected"
                         + f" {expected_shape[2]}"
                     )
+                for i, v in enumerate(default_value):
+                    param[:, :, i] = float(v)
             elif isinstance(default_value, torch.Tensor):
                 # if tensor, then use the same tensor for all joints
                 if tuple(default_value.shape) == expected_shape: