Smart Event Lists for Simulation

How to Handle Time-Driven Tasks in Isaac Sim

smart-event-lists-for-simulation

Last updated:

July 11, 2025

In any simulation — especially for digital twins, robotics, or industrial AI — time-driven task execution is the backbone of realism. If your events are out of order or blocked by a delayed robot, things fall apart fast.

To handle this, we use a linked event list: a dynamic structure that schedules, reorders, and executes simulation tasks — with time as the driving force.

In this post, we’ll show you how to:

✅ Build a minimal task/event system for Isaac Sim
✅ Support dynamic reordering (when one task is blocked)
✅ Synchronize with simulation time
✅ Apply it to digital twin use cases like pick-and-place, condition monitoring, and maintenance alerts

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🔁 What Is an Event List in Simulation?

An event list is a data structure that holds simulation actions, each with:

A timestamp
A callback or action
A resource lock (optional — e.g. robot ID)

You can think of it as a smart queue. Instead of blindly stepping forward, we:

Check: Is the event ready?
If yes → execute
If no → skip and try later

🧪 Example Use Case: Pick-and-Place in Isaac Sim

Let’s say we want to simulate a warehouse robot performing scheduled tasks:

At t=1.0, move to bin A
At t=2.5, pick up object
At t=3.0, place at station B
At t=3.1, scan for next task

Now let’s build a linked event list to drive this simulation.

🧰 Minimal Python Code in Isaac Sim

import carb
import omni
import heapq
from pxr import Gf

class Event:
    def __init__(self, time, action, name="", requires=None):
        self.time = time
        self.action = action
        self.name = name
        self.requires = requires or []

    def __lt__(self, other):  # For heapq priority
        return self.time < other.time

class EventQueue:
    def __init__(self):
        self.events = []
        self.resources = {}

    def add_event(self, event: Event):
        heapq.heappush(self.events, event)

    def tick(self, sim_time):
        while self.events and self.events[0].time <= sim_time:
            event = heapq.heappop(self.events)
            if self._resources_ready(event):
                print(f"[{sim_time:.2f}s] Executing: {event.name}")
                self._lock_resources(event)
                event.action()
                self._unlock_resources(event)
            else:
                # Delay event slightly and re-insert
                event.time += 0.1
                print(f"[{sim_time:.2f}s] Delayed: {event.name}")
                self.add_event(event)

    def _resources_ready(self, event):
        return all(not self.resources.get(r, False) for r in event.requires)

    def _lock_resources(self, event):
        for r in event.requires:
            self.resources[r] = True

    def _unlock_resources(self, event):
        for r in event.requires:
            self.resources[r] = False

🕹 Using This in Isaac Sim

from omni.isaac.core import SimulationContext
import time

sim_context = SimulationContext()
event_queue = EventQueue()

# Example actions
def move_robot():
    print("Moving robot...")

def pick_object():
    print("Picking object...")

def place_object():
    print("Placing object...")

event_queue.add_event(Event(1.0, move_robot, "Move to Bin A", requires=["robot"]))
event_queue.add_event(Event(2.5, pick_object, "Pick Object", requires=["robot"]))
event_queue.add_event(Event(3.0, place_object, "Place at Station", requires=["robot"]))

sim_context.play()

while sim_context.is_playing():
    sim_time = sim_context.current_time
    event_queue.tick(sim_time)
    sim_context.step(render=True)

🔧 What This Enables in Digital Twins

This pattern works for more than robots:

Condition-based maintenance: Trigger tasks when sensor readings exceed thresholds
Energy simulation: Turn on/off loads based on schedules
Production line orchestration: Simulate multi-robot systems with blocking and dependencies

It scales naturally to:

🚀 Multi-agent simulations
📊 Predictive analytics
🧪 Real-time what-if testing

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🏁 Final Thoughts

Simulation isn’t just about rendering — it’s about control and causality.
With a smart event list driving your simulation, you can handle delays, dependencies, and timing with confidence.

Want help integrating this kind of logic into your Champion or Isaac Sim digital twin workflow?
We specialize in real-time task orchestration, CAD-to-shell conversion, and AI-driven simulation automation.

👉 Let’s talk.

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