change min_frequency to global step counter

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/direct_rl_env.py

@@ -519,7 +519,8 @@ def _reset_idx(self, env_ids: Sequence[int]):
         # apply events such as randomization for environments that need a reset
         if self.cfg.events:
             if "reset" in self.event_manager.available_modes:
-                self.event_manager.apply(env_ids=env_ids, mode="reset", dt=self.step_dt)
+                env_step_count = self._sim_step_counter // self.cfg.decimation
+                self.event_manager.apply(env_ids=env_ids, mode="reset", global_env_step_count=env_step_count)
 
         # reset noise models
         if self.cfg.action_noise_model:

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/manager_based_env.py

@@ -341,7 +341,8 @@ def _reset_idx(self, env_ids: Sequence[int]):
         self.scene.reset(env_ids)
         # apply events such as randomizations for environments that need a reset
         if "reset" in self.event_manager.available_modes:
-            self.event_manager.apply(env_ids=env_ids, mode="reset", dt=self.step_dt)
+            env_step_count = self._sim_step_counter // self.cfg.decimation
+            self.event_manager.apply(env_ids=env_ids, mode="reset", global_env_step_count=env_step_count)
 
         # iterate over all managers and reset them
         # this returns a dictionary of information which is stored in the extras

source/extensions/omni.isaac.lab/omni/isaac/lab/envs/manager_based_rl_env.py

@@ -319,7 +319,8 @@ def _reset_idx(self, env_ids: Sequence[int]):
         self.scene.reset(env_ids)
         # apply events such as randomizations for environments that need a reset
         if "reset" in self.event_manager.available_modes:
-            self.event_manager.apply(env_ids=env_ids, mode="reset", dt=self.step_dt)
+            env_step_count = self._sim_step_counter // self.cfg.decimation
+            self.event_manager.apply(env_ids=env_ids, mode="reset", global_env_step_count=env_step_count)
 
         # iterate over all managers and reset them
         # this returns a dictionary of information which is stored in the extras

source/extensions/omni.isaac.lab/omni/isaac/lab/managers/event_manager.py

@@ -119,7 +119,13 @@ def reset(self, env_ids: Sequence[int] | None = None) -> dict[str, float]:
         # nothing to log here
         return {}
 
-    def apply(self, mode: str, env_ids: Sequence[int] | None = None, dt: float | None = None):
+    def apply(
+        self,
+        mode: str,
+        env_ids: Sequence[int] | None = None,
+        dt: float | None = None,
+        global_env_step_count: int | None = None,
+    ):
         """Calls each event term in the specified mode.
 
         Note:
@@ -132,6 +138,8 @@ def apply(self, mode: str, env_ids: Sequence[int] | None = None, dt: float | Non
                 Defaults to None, in which case the event is applied to all environments.
             dt: The time step of the environment. This is only used for the "interval" mode.
                 Defaults to None to simplify the call for other modes.
+            global_env_step_count: The environment step count of the task. This is only used for the "reset" mode.
+                Defaults to None to simplify the call for other modes.
 
         Raises:
             ValueError: If the mode is ``"interval"`` and the time step is not provided.
@@ -173,17 +181,18 @@ def apply(self, mode: str, env_ids: Sequence[int] | None = None, dt: float | Non
                         time_left[env_ids] = torch.rand(len(env_ids), device=self.device) * (upper - lower) + lower
             # check for minimum frequency for reset
             elif mode == "reset":
-                if dt is None:
+                if global_env_step_count is None:
                     raise ValueError(
-                        f"Event mode '{mode}' requires the time step of the environment"
+                        f"Event mode '{mode}' requires the step count of the environment"
                         " to be passed to the event manager."
                     )
-                self._reset_mode_time_until_next_reset[index] -= dt
+
                 if env_ids is not None and len(env_ids) > 0:
-                    time_left = self._reset_mode_time_until_next_reset[index]
-                    env_ids = env_ids[time_left[env_ids] <= 0.0]
+                    last_reset_step = self._reset_mode_last_reset_step_count[index]
+                    steps_since_last_reset = global_env_step_count - last_reset_step
+                    env_ids = env_ids[steps_since_last_reset[env_ids] >= term_cfg.min_step_count_between_reset]
                     if len(env_ids) > 0:
-                        time_left[env_ids] = term_cfg.min_frequency
+                        last_reset_step[env_ids] = global_env_step_count
                     else:
                         # no need to call func to sample
                         continue
@@ -250,8 +259,8 @@ def _prepare_terms(self):
         self._interval_mode_time_left: list[torch.Tensor] = list()
         # global timer for "interval" mode for global properties
         self._interval_mode_time_global: list[torch.Tensor] = list()
-        # buffer to store the time until next reset for each environment for "reset" mode with minimum frequency
-        self._reset_mode_time_until_next_reset: list[torch.Tensor] = list()
+        # buffer to store the step count when reset was last performed for each environment for "reset" mode
+        self._reset_mode_last_reset_step_count: list[torch.Tensor] = list()
 
         # check if config is dict already
         if isinstance(self.cfg, dict):
@@ -304,5 +313,5 @@ def _prepare_terms(self):
                     self._interval_mode_time_left.append(time_left)
 
             elif term_cfg.mode == "reset":
-                time_left = torch.zeros(self.num_envs, device=self.device)
-                self._reset_mode_time_until_next_reset.append(time_left)
+                step_count = torch.zeros(self.num_envs, device=self.device, dtype=torch.int32)
+                self._reset_mode_last_reset_step_count.append(step_count)

source/extensions/omni.isaac.lab/omni/isaac/lab/managers/manager_term_cfg.py

@@ -209,11 +209,11 @@ class EventTermCfg(ManagerTermBaseCfg):
         This is only used if the mode is ``"interval"``.
     """
 
-    min_frequency: float = 0.0
-    """The minimum time in seconds between when term is applied.
+    min_step_count_between_reset: float = 0.0
+    """The minimum number of environment steps between when term is applied.
 
     When mode is "reset", the term will not be applied on the next reset unless
-    the time since the last application of the term has exceeded this.
+    the number of steps since the last application of the term has exceeded this.
 
     Note:
         This is only used if the mode is ``"reset"``.

source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/direct/shadow_hand/shadow_hand_env_cfg.py

@@ -29,7 +29,7 @@ class EventCfg:
     robot_physics_material = EventTerm(
         func=mdp.randomize_rigid_body_material,
         mode="reset",
-        min_frequency=36.0,
+        min_step_count_between_reset=720,
         params={
             "asset_cfg": SceneEntityCfg("robot"),
             "static_friction_range": (0.7, 1.3),
@@ -40,7 +40,7 @@ class EventCfg:
     )
     robot_joint_stiffness_and_damping = EventTerm(
         func=mdp.randomize_actuator_gains,
-        min_frequency=36.0,
+        min_step_count_between_reset=720,
         mode="reset",
         params={
             "asset_cfg": SceneEntityCfg("robot", joint_names=".*"),
@@ -52,7 +52,7 @@ class EventCfg:
     )
     robot_joint_limits = EventTerm(
         func=mdp.randomize_joint_parameters,
-        min_frequency=36.0,
+        min_step_count_between_reset=720,
         mode="reset",
         params={
             "asset_cfg": SceneEntityCfg("robot", joint_names=".*"),
@@ -64,7 +64,7 @@ class EventCfg:
     )
     robot_tendon_properties = EventTerm(
         func=mdp.randomize_fixed_tendon_parameters,
-        min_frequency=36.0,
+        min_step_count_between_reset=720,
         mode="reset",
         params={
             "asset_cfg": SceneEntityCfg("robot", fixed_tendon_names=".*"),
@@ -78,7 +78,7 @@ class EventCfg:
     # -- object
     object_physics_material = EventTerm(
         func=mdp.randomize_rigid_body_material,
-        min_frequency=36.0,
+        min_step_count_between_reset=720,
         mode="reset",
         params={
             "asset_cfg": SceneEntityCfg("object"),
@@ -90,7 +90,7 @@ class EventCfg:
     )
     object_scale_mass = EventTerm(
         func=mdp.randomize_rigid_body_mass,
-        min_frequency=36.0,
+        min_step_count_between_reset=720,
         mode="reset",
         params={
             "asset_cfg": SceneEntityCfg("object"),