Integrating Robotic Intelligence Across Multiple Platforms

Decoupling simulation: How to inject Omniverse APIs directly into your existing robotics stack If you are an enterprise architect building Physical AI pipelines, you already know the biggest friction point of digital twin adoption: platform lock-in. Historically, to get access to high-fidelity physics or RTX rendering, you had to adopt an entire monolithic application framework. You couldn't easily deploy it headlessly, and integrating it into an existing CI/CD system or custom robotics stack was an architectural headache. At GTC, we fundamentally changed this. We are transitioning NVIDIA Omniverse to a modular, library-first architecture. Instead of forcing you into a single runtime container, we are exposing the core Omniverse engines as standalone, headless-first C APIs (with Python and C++ bindings): 🟢 ovphysx: High-speed, USD-native physics simulation. You can now inject hardware-accelerated physics directly into your existing control loops without carrying the weight of a full UI. 🟢 ovrtx: RTX path-tracing and sensor simulation as a library. You can load a USD scene, step the sensor simulation, and return the synthetic data directly into NumPy or PyTorch pipelines using zero-copy DLPack transfers. 🟢 ovstorage: API-driven data pipelines that connect your existing PLM/PDM storage infrastructure directly to the ecosystem, avoiding massive manual data migrations. This modularity is exactly what enables Agentic AI to scale. By utilizing the Model Context Protocol (MCP), LLM-based agents can now execute these standalone libraries directly. Tools like Claude or an OpenClaw agent can autonomously load a USD scene, step the physics simulation, and edit parameters without human UI interaction. You no longer have to adopt the Omniverse application framework to use Omniverse capabilities. The early access libraries are available on GitHub and NGC today. 🔗 Read the full engineering breakdown of the new API structures here: https://lnkd.in/gG5wW4DY If you are currently building simulation stacks, what is your biggest integration hurdle when tying third-party physics into your existing CI/CD pipelines? 👇 #NVIDIA #Omniverse #PhysicalAI #OpenUSD #EnterpriseArchitecture #DevRel #Robotics #AgenticAI

Happy to share our latest paper, "Enabling Novel Mission Operations and Interactions with ROSA: The Robot Operating System Agent". This work was led by Rob R. in collaboration with Marcel Kaufmann, Jonathan Becktor, Sangwoo Moon, Kalind Carpenter, Kai Pak, Amanda Towler, Rohan Thakker and myself. Please find the #OpenSource code, paper, and video demonstration linked below. Operating autonomous robots in the field is often challenging, especially at scale and without the proper support of Subject Matter Experts (SMEs). Traditionally, robotic operations require a team of specialists to monitor diagnostics and troubleshoot specific modules. This dependency can become a bottleneck when an SME is unavailable, making it difficult for operators to not only understand the system's functional state but to leverage its full capability set. The challenge grows when scaling to 1-to-N operator-to-robot interactions, particularly with a heterogeneous robot fleet (e.g., walking, roving, flying robots). To address this, we present the ROSA framework, which can leverage state-of-the-art Vision Language Models (VLMs), both on-device and online, to present the autonomy framework's capabilities to operators in an intuitive and accessible way. By enabling a natural language interface, ROSA helps bridge the gap for operators who are not roboticists, such as geologists or first responders, to effectively interact with robots in real-world missions. In our video, we demonstrate ROSA using the NeBula Autonomy framework developed at NASA Jet Propulsion Laboratory to operate in JPL's #MarsYard. Our paper also showcases ROSA's integration with JPL's EELS (Exobiology Extant Life Surveyor) robot and the NVIDIA Carter robot in the IsaacSim environment (stay tuned for ROSA IssacSim extension updates!). These examples highlight ROSA's ability to facilitate interactions across diverse robotic platforms and autonomy frameworks. Paper: https://lnkd.in/g4PRjF4V Github: https://lnkd.in/gwWXmmjR Video: https://lnkd.in/gxKcum27 #Robotics #Autonomy #AI #ROS #FieldRobotics #RobotOperations #NaturalLanguageProcessing #LLM #VLM

AI adoption often begins with enthusiasm and experimentation, but without coordination it can quickly become fragmented. Separate tools, models, and data pipelines make it difficult for enterprises to scale efficiently or maintain consistent governance. Recognizing these challenges, Intel Corporation IT set out to build a unified foundation that could scale securely while delivering measurable business value.     To meet that need, we developed One AI (1AI), a consolidated agentic AI platform that brings multiple chatbot interfaces and language models together within a single, modular framework. Built on Intel architecture and open-source tools, One AI enables business units to deploy specialized agents for specific use cases under consistent, centralized oversight. The unified interface improves the user experience and simplifies maintenance, helping reduce the complexity that often accompanies distributed AI projects.      Working with Intel’s Sales and Marketing Group, we were able to identify high-value use cases and implemented a centralized framework that eliminated redundancy and improved response times and overall quality. This collaboration demonstrated how a well-governed, agentic AI platform can accelerate productivity and align technical innovation with business outcomes.     The success of One AI marks a step toward a mature, scalable approach to agentic AI. One that embeds automation within enterprise processes while preserving trust across every layer of the system. As we expand One AI across additional business environments, this model will continue to shape how organizations use AI to improve efficiency and decision quality at scale. 

🔥 We've been battling the same integration challenges for decades. Every platform speaks its own language, requiring custom APIs for every connection, with protocols that constantly shift with each update. There may finally be a path forward. Enter MCP (Model Context Protocol). Think of it this way: 🌐 HTTP became the universal standard that lets humans interact with websites consistently, regardless if you were doing bank transactions or equipment configuration. 🤖 MCP is emerging as the standard that allows AI systems to interface with virtually any platform or tool in a similar consistent way. Major players are already moving - Microsoft is integrating MCP into Copilot Studio, and OpenAI officially adopted it across their platform in March. For any industry dealing with complex system integrations, this represents a fundamental shift. Instead of building custom bridges between every system where every skill or capability needs to be explicitly planned for, we're moving toward a world where AI can seamlessly connect and orchestrate across platforms using this common protocol, with self awareness of capabilities. At CTI, our team is 🚀 hands-on exploring MCP's potential and building the expertise to deploy it strategically. We're not just watching from the sidelines—we're actively integrating it into projects and leveraging this technology, even as it still evolves into a mature standard. The implications extend far beyond any single industry. This could reshape how we think about system architecture, reduce integration costs, and unlock capabilities we haven't even imagined yet. I'm 💡curious to hear who else is exploring MCP and what potential you're finding. I'm confident that this is a pivotal moment worth paying attention to. #MCP #Integration #AI #Innovation #TechLeadership #AVTweeps #MicrosoftCopilot

With the global AI market now valued at nearly $400 billion, and 97 million people working in the field this year, AI adoption is reaching new heights, transforming industries worldwide. The robotics sector is booming in parallel — expected to more than double in size from $71.78 billion in 2025 to $150.84 billion by 2030, fueled by the integration of advanced AI that enables sophisticated autonomy and real-time adaptation. An astonishing 83% of companies rank AI as a top priority in their business strategies, further accelerating the pace of innovation. Against this backdrop of rapid advancement, Skild AI, a robotics startup backed by Amazon and SoftBank, has just unveiled its general-purpose AI model, Skild Brain, designed to operate seamlessly across various types of robots—from factory machines to humanoids. According to Reuters, Skild’s model enables robots to "think, navigate and respond more like humans," marking a major leap toward versatile physical AI. Real-world demonstrations have shown Skild-powered robots climbing stairs, maintaining their balance when pushed, and retrieving items from chaotic environments—capabilities that demand true spatial awareness, dexterity, and adaptability. Key innovations from Skild AI’s approach include: 1. Unified "shared brain": Robots using Skild Brain share and contribute data, continually enhancing the collective model’s intelligence and functionality. 2. Humanlike adaptability: Robust performance in diverse and unstructured environments—moving beyond today’s narrow, single-task robots. 3. Safety and collaborative design: Built-in safeguards to control force and enable safe operation alongside humans in industrial and service settings. 4. Ecosystem traction: Early customers include LG CNS and unnamed logistics and industrial partners, signaling real-world validation. As we witness this transformative moment, it’s clear that the convergence of AI and robotics is poised to redefine the future of work, automation, and human-machine collaboration. How do you see general-purpose AI and robotics impacting your industry or daily life in the years ahead?

As organizations accelerate their AI adoption, the need for flexible, scalable, and secure platforms becomes paramount. My previous article, https://lnkd.in/gGuK4j7q , represented two powerful yet distinct approaches to building AI agents. While Copilot Studio offers a low-code/no-code interface for rapid deployment, targeting any kind of business user, Azure AI Foundry provides a pro-code environment with deep customization and orchestration capabilities, targeting developer audiences.   But what if you would not need to decide between one or the other, but benefit from integrating both platforms and unlock transformative business value across all teams? This is exactly the question I got asked increasingly while I was teaching our “Copilot, Copilot Studio and Azure AI Foundry” Instructor Led Training courses as a Microsoft Technical Trainer.   This follow-up article (https://lnkd.in/gGbRgkBu ) starts with the business rationale for integration. From there, I will continue with detailing the influence of cost and ROI parameters as part of decision-making. Last, I will guide you through multiple technical integration capabilities available today, and how both platforms can complement each other. #SkilledbyMTT

Recent progress in embodied intelligence shows a clear shift from task-specific automation toward foundation models. These models integrate perception, language, planning, and control across manipulation, navigation, and reasoning tasks. This survey shows how multimodal foundation models already enable zero-shot generalization, language-guided manipulation, and long-horizon task planning on real robots, not just in simulation. I think the real breakthrough is not scale alone. It is the ability to close the perception-action loop using world models that combine vision, language, and physical dynamics. The next frontier will be hardware-agnostic robot intelligence, learning from dynamic data, and strong alignment between AI reasoning and physical execution. #Robotics #FoundationModels #EmbodiedAI #RobotLearning #IndustrialAI #AutonomousSystems #MultimodalAI #Siemens