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<!-- Document Title
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============================================= -->
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<title>Simbotic - Bridging the reality gap</title>
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<meta name="description" content="SIMBOTIC is A modern 3D simulation engine and AI platform designed for bridging the reality gap between simulation and the real-world.
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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.
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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.">
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<meta property="og:title" content="Simbotic - Simulation Engine and AI Platform" />
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<title>Simbotic - Next-Generation AI Simulation Platform</title>
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<meta name="description" content="Simbotic is an advanced AI simulation platform that bridges the gap between virtual and physical worlds. Through digital twins and synthetic data generation, we enable AI systems to develop spatial intelligence and learn from embodied experiences, powering the next generation of robotics, autonomous systems, and intelligent agents.">
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<meta property="og:title" content="Simbotic - Next-Generation AI Simulation Platform" />
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<meta property="og:type" content="website" />
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<meta property="og:url" content="https://simbotic.github.io/index.html" />
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<meta property="og:image"
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content="https://gitcdn.xyz/cdn/Simbotic/Simbotic.github.io/master/images/vertex/Simbotic_Artboard_Earth.jpg" /><!---->
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<meta property="og:description" content="Simulation engine bridging the reality gap">
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<meta property="og:description" content="Advanced simulation platform enabling AI to learn through physical interaction and spatial reasoning">
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<meta name="keywords" content="Simbotic, Simbotic ia, Simbotic Engine, IA, artificial, intelligence, artificial intelligence, Nvidia, UE4, Unreal Engine, Simulation, Machine Learning, Synthetic Data, Pipelines, robots,">
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<meta name="keywords" content="Simbotic, Simbotic AI, Simbotic Engine, Artificial Intelligence, NVIDIA, UE5, Unreal Engine, Simulation, Machine Learning, Synthetic Data, Data Pipelines, Robotics, Multi-Agent Reinforcement Learning, Computer Vision, Autonomous Systems, Generative AI, AI Agents, Digital Twins, Domain Randomization, Physical AI, Agentic AI, Embodied AI, Spatial AI, LLMs, Large Language Models">
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<meta name="author" content="info@vertexstudio.co">
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<meta name="owner" content="vertex Studio">
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<meta name="twitter:card" content="summary_large_image">
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<meta name="twitter:title" content="Simbotic - Simulation Engine and AI Platform">
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<meta name="twitter:title" content="Simbotic - Next-Generation AI Simulation Platform">
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<meta name="twitter:creator" content="Vertex Studio">
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<meta name="twitter:url" content="https://simbotic.github.io/index.html">
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<meta name="twitter:domain" content="https://simbotic.github.io">
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<meta name="twitter:image"
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content="https://gitcdn.xyz/cdn/Simbotic/Simbotic.github.io/master/images/vertex/Simbotic_Artboard_Earth.jpg"><!---->
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<meta name="twitter:description" content="Simulation engine bridging the reality gap">
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<meta name="twitter:description" content="Advanced simulation platform enabling AI to learn through physical interaction and spatial reasoning">
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<meta name="google-site-verification" content="WBqUc3OYoMeUQf3yMDzkiCfsmBE8z9y9kuBCAF3mwYM" />
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@@ -142,21 +138,20 @@ <h1 class="simbotic-intro-title tittle highlight-phrase">
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</h1>
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<div class="simbotic-intro-text">
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<p>
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A modern 3D simulation engine and AI platform designed for <span class="accent highlight-phrase skew">bridging the
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reality gap</span> between simulation and the
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real-world.
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An advanced simulation platform that <span class="accent highlight-phrase skew">bridges virtual and physical worlds</span>,
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enabling AI systems to learn through realistic interaction and environmental understanding.
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</p>
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<br />
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<p>
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Machine learning is a critical component in building autonomous and robotic systems, yet
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requires prohibitively high amounts of data and many hours of experience to learn intelligent
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behaviors.
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While large language models have revolutionized AI, they often struggle with physical reasoning
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and three-dimensional understanding. Real-world robotics and autonomous systems require
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spatial perception, motion prediction, and the ability to learn from physical interaction.
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</p>
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<br />
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<p>
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Simbotic solves both of these problems by generating infinite amounts of synthetic data
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for AI models and providing highly realistic environments for intelligent agents to acquire
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experience.
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Simbotic provides the infrastructure for AI to develop these capabilities through high-fidelity
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digital twins and unlimited synthetic training scenarios, accelerating the path from simulation
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to real-world deployment.
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</p>
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</div>
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</div>
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<div class=" col-md-4 col-sm-12">
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<div class="DivThink">
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<p class="TxtMain TxtBubble">
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Simbotic has been catapulted by Epic MegaGrants into serious development. Stay with us while we flesh out plugins, documentation and tutorials.
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Contact us if you want to explore integrating Unreal Engine into robotics, simulation pipelines, intelligent video analytics, or any other computer vision project.
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Simbotic has been accelerated by Epic MegaGrants funding. We're actively developing plugins, extensive documentation, and tutorials.
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Contact us to explore how Unreal Engine 5 can transform your robotics, AI training, or autonomous systems projects.
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</p>
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</div>
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</div>
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</div>
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<div class=" col-md-4 col-sm-12">
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<p>
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Simbotic powers dynamic multiagent training environments for deep reinforcement learning
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agents. From abstract, focused on physics dynamics; to photorealistic, focused on computer
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vision.
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Simbotic creates dynamic multi-agent environments where AI learns to navigate, manipulate,
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and reason about physical spaces. From physics simulations that teach mechanical understanding
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to photorealistic scenes that train computer vision systems.
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</p>
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<br />
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<p>
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Multiagent environments are critical for teaching intelligent systems how to reason about
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the world around them; learn how to react to real world events, develop strategies, tune
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controls, sensors and algorithms.
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These environments allow AI to develop crucial capabilities: understanding object relationships,
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predicting trajectories, planning movements, and learning from trial-and-error. These are skills
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that language models alone cannot provide.
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</p>
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<br />
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<p>
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These environments provide accelerated ground-truth and scripting of complex missions that
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are expensive and risky in the real world, yet cheap and safe in simulation.
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With perfect ground-truth data and infinitely configurable scenarios, complex behaviors
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that would take years to learn in reality can be mastered in days of accelerated simulation.
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</p>
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</div>
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</div>
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<div class=" col-md-4 col-sm-12 video-text-bottom-space">
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<p>
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In absence of real world data, virtually constructed datasets can bridge the gap. Simbotic
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AI can create conditions that generate synthetic datasets to be used by AI models.
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When real-world data is scarce or dangerous to collect, Simbotic generates unlimited
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synthetic datasets from virtual scenarios that mirror reality with precise accuracy.
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</p>
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<br />
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<p>
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Not only can this synthetized data be randomized and dynamic, it's context-aware of the
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domain it's being generated in. This structured domain randomization is key for generalizing
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all kinds of machine learning models.
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Through domain randomization (varying lighting, textures, and physics parameters), AI models
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learn to generalize beyond specific scenarios, developing robust capabilities that
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transfer seamlessly to the real world.
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</p>
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<br />
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<p>
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Simbotic also takes care of simulating sensors and dynamical systems, generating metadata
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and labeling; significantly contributing to the data pipelining process.
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Leveraging generative AI including diffusion models and neural radiance fields (NeRFs),
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Simbotic creates diverse 3D environments and assets, exponentially expanding training possibilities.
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</p>
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</div>
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<div class=" col-md-4 col-sm-12">
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</div>
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<div class=" col-md-4 col-sm-12 spacing">
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<h1 class="tittle highlight-phrase">
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Human in the Loop
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Human-AI Collaboration
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</h1>
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</div>
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<div class=" col-md-4 col-sm-12">
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</div>
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<div class=" col-md-4 col-sm-12">
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<p>
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Human-in-the-loop is a symbiotic relationship between human and artificial intelligence. In
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this approach, humans are directly involved in the training, tuning and testing of machine
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learning algorithms.
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Humans and AI work together in a powerful feedback loop. Expert operators can demonstrate
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complex behaviors, correct mistakes, and teach nuanced skills that pure automation struggles
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to discover.
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</p>
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<br />
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<p>
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Humans can jump inside the simulation and change the fate of the world. Humans can trigger
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incredible accelerations by sharing domain expertise and dexterity, from which agents can
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learn.
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By stepping into simulations, humans can guide AI through challenging scenarios, demonstrate
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optimal strategies, and transfer years of expertise in minutes. This dramatically accelerates
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the learning process.
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</p>
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<br />
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<p>
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When joining scalable simulation sessions, humans can jump between dimensions
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and trigger exponential learning. Effectively transferring years of domain knowledge in a
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matter of
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minutes.
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This collaborative approach is essential for developing trustworthy autonomous systems
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that can handle edge cases, adapt to unexpected situations, and align with human
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intentions and values.
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</p>
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</div>
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</div>
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<div class=" col-md-4 col-sm-12 video-text-bottom-space">
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<p>
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Simbotic is powered by Unreal Engine 4, GStreamer, PyTorch and NVidia technologies.
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End-to-end accelerated pipelines to make sure data reaches every system with the least
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latency possible.
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Powered by Unreal Engine 5 for stunning visual fidelity, NVIDIA technologies for GPU acceleration,
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and PyTorch for deep learning, Simbotic delivers the computational performance needed for
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real-time AI training.
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</p>
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<br />
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<p>
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Custom GStreamer plugins, like UE4 gst app source and sink, opens up Simbotic data to fast
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and flexible real time streaming pipelines.
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Custom streaming pipelines built with GStreamer and performance-critical components in Rust
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ensure minimal latency between simulation and learning, enabling real-time decision making
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even in complex multi-agent scenarios.
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</p>
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<p>
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Containerized simulations are ready to be massively operated with Kubernetes. Focusing on
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tools and best practices for building machine learning at scale in the real world.
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Fully containerized and Kubernetes-ready, Simbotic scales from single experiments to
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massive parallel training runs, bringing enterprise-grade reliability to AI development
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workflows.
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</p>
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<div class=" col-md-4 col-sm-12"></div>
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<div class="DivThink">
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<p class="TxtMain TxtBubble"> Supports receiving and sending from several thirdparty <span
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class="accent">media and robotics</span> applications.</p>
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<p class="TxtMain TxtBubble"> Facilitates bidirectional interfacing with prominent <span
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class="accent">media and robotics</span> ecosystems to extend LLM capabilities.</p>
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</div>
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<figure>
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<img class="Crsl_img img-fluid rounded mx-auto d-block" src="Extern/Image/ros.jpg"
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data-color="firebrick" alt="Second Image">
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<figcaption class="figcap highlight-phrase center" style="z-index:9999 ">ROS URDF and visualizing with
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RViz
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<figcaption class="figcap highlight-phrase center" style="z-index:9999 ">ROS 2 URDF with RViz visualization
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for spatial and embodied AI
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</figcaption>
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</figure>
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</div>
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<figure>
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<img class="Crsl_img img-fluid rounded mx-auto d-block" src="Extern/Image/scalable.jpg"
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data-color="firebrick" alt="Second Image">
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<figcaption class="figcap highlight-phrase center" style="z-index:9999">Dockerized containers ready to
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scale with Kubernetes
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<figcaption class="figcap highlight-phrase center" style="z-index:9999">Cloud-native deployments scalable
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with Kubernetes
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</figcaption>
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</figure>
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</div>
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<div class="col-md-3 col-sm-12">
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<figure>
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<img class="Crsl_img img-fluid rounded mx-auto d-block" src="Extern/Image/px4.jpg"
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data-color="firebrick" alt="Second Image">
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<figcaption class="figcap highlight-phrase center" style="z-index:9999">Software and Hardware in the
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loop
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<figcaption class="figcap highlight-phrase center" style="z-index:9999">SIL/HIL integration with PX4 and
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advanced flight controllers
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</figcaption>
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</figure>
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</div>

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