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A modern 3D simulation engine and AI platform designed for bridging the reality gap between simulation and the real-world.

Machine learning is a critical component in building autonomous and robotic systems, yet requires prohibitively high amounts of data and many hours of experience to learn intelligent behaviors.

Simbotic solves both of these problems by generating infinite amounts of synthetic data for AI models and providing highly realistic environments for intelligent agents to acquire experience.

GStreamer PyTorch NVidia Unreal Engine

Epic MegaGrants Recipient

Simbotic has been catapulted by Epic MegaGrants into serious development. Stay with us while we flesh out plugins, documentation and tutorials. Contact us if you want to explore integrating Unreal Engine into robotics, simulation pipelines, intelligent video analytics, or any other computer vision project.


Simbotic powers dynamic multiagent training environments for deep reinforcement learning agents. From abstract, focused on physics dynamics; to photorealistic, focused on computer vision.

Multiagent environments are critical for teaching intelligent systems how to reason about the world around them; learn how to react to real world events, develop strategies, tune controls, sensors and algorithms.

These environments provide accelerated ground-truth and scripting of complex missions that are expensive and risky in the real world, yet cheap and safe in simulation.

Synthetic Data

In absence of real world data, virtually constructed datasets can bridge the gap. Simbotic AI can create conditions that generate synthetic datasets to be used by AI models.

Not only can this synthetized data be randomized and dynamic, it's context-aware of the domain it's being generated in. This structured domain randomization is key for generalizing all kinds of machine learning models.

Simbotic also takes care of simulating sensors and dynamical systems, generating metadata and labeling; significantly contributing to the data pipelining process.

Human in the Loop

Human-in-the-loop is a symbiotic relationship between human and artificial intelligence. In this approach, humans are directly involved in the training, tuning and testing of machine learning algorithms.

Humans can jump inside the simulation and change the fate of the world. Humans can trigger incredible accelerations by sharing domain expertise and dexterity, from which agents can learn.

When joining scalable simulation sessions, humans can jump between dimensions and trigger exponential learning. Effectively transferring years of domain knowledge in a matter of minutes.

Accelerated Pipelines

Simbotic is powered by Unreal Engine 4, GStreamer, PyTorch and NVidia technologies. End-to-end accelerated pipelines to make sure data reaches every system with the least latency possible.

Custom GStreamer plugins, like UE4 gst app source and sink, opens up Simbotic data to fast and flexible real time streaming pipelines.

Containerized simulations are ready to be massively operated with Kubernetes. Focusing on tools and best practices for building machine learning at scale in the real world.

ThirdParty Integrations

Supports receiving and sending from several thirdparty media and robotics applications.

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ROS URDF and visualizing with RViz
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Open Sound Control I/O
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Dockerized containers ready to scale with Kubernetes
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Software and Hardware in the loop

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