Platform-Driven Approaches for Robotics Development

𝗙𝗿𝗼𝗺 𝗥𝗢𝗦 𝘁𝗼 𝗟𝗲𝗥𝗼𝗯𝗼𝘁: 𝗛𝗼𝘄 𝗔𝗿𝗲 𝗧𝗲𝗮𝗺𝘀 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗩𝗟𝗔 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀? Most real-world robotics systems are built on pub/sub architectures like #ROS. Sensors and estimators publish asynchronously and at different rates: • Cameras at ~30 Hz • Perception at ~10 Hz • State, control, and actions all run on their own clocks This decoupled design has powered robotics for decades. Vision-Language-Action models like NVIDIA Robotics GR00T and Physical Intelligence pi0 work differently. For both training and inference, they require synchronized, tensor-based data with aligned observations, states, and actions on a shared timeline. Hugging Face's #LeRobot has emerged as the community standard for representing this kind of training data. It is PyTorch-native, well documented, and increasingly supported across the ecosystem. The hard part is the bridge from asynchronous ROS topics to synchronized LeRobot episodes, without introducing bias or artifacts. At Roboto AI, we see a few common approaches in practice: 1) 𝗥𝗮𝘄 𝗥𝗢𝗦𝗯𝗮𝗴 𝗼𝗿 𝗠𝗖𝗔𝗣, 𝘁𝗵𝗲𝗻 𝗼𝗳𝗳𝗹𝗶𝗻𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝘁𝗼 𝗟𝗲𝗥𝗼𝗯𝗼𝘁 ✔ Maximum data fidelity and the ability to reprocess later ✘ Timestamp handling, resampling, interpolation, and episode definition all need real care 2) 𝗢𝗻𝗹𝗶𝗻𝗲 𝘀𝘆𝗻𝗰𝗵𝗿𝗼𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗱𝗶𝗿𝗲𝗰𝘁 𝗟𝗲𝗥𝗼𝗯𝗼𝘁 𝘄𝗿𝗶𝘁𝗶𝗻𝗴 ✔ Training-ready data immediately ✘ Synchronization choices are locked in once data is recorded 3) 𝗛𝘆𝗯𝗿𝗶𝗱 𝗰𝗮𝗽𝘁𝘂𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝗿𝗮𝘄 𝗯𝗮𝗴𝘀 𝗽𝗹𝘂𝘀 𝗮 𝘀𝘆𝗻𝗰𝗵𝗿𝗼𝗻𝗶𝘇𝗲𝗱 𝗱𝗮𝘁𝗮𝘀𝗲𝘁 ✔ Fast iteration with reproducibility ✘ Higher storage costs and more operational complexity 4) 𝗖𝘂𝘀𝘁𝗼𝗺, 𝗻𝗼𝗻-𝗥𝗢𝗦 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 ✔ Full control over data primitives ✘ You end up re-implementing large parts of the robotics stack The most common failure mode we see is train-inference skew between offline preprocessing and live data flow. This problem exists across ML, but it becomes especially critical when observations map directly to robot actions. Typical causes include: • Different resampling or alignment logic • Implicit lookahead during offline conversion • Episode boundaries that do not match deployment The result is strong offline metrics and disappointing real-world behavior. Despite the push toward end-to-end learning, most production robots will continue to rely on ROS-style pub/sub systems for the foreseeable future. That makes reproducible and auditable data curation the key link between robotics stacks and VLA training. At Roboto, we are actively building tooling to go from raw robotics data to ML-ready datasets. If you are working on VLA pipelines and have wrestled with this gap, I would love to compare notes.

Just came back from Germany meeting with one of our F500 clients, and their automation team gave me a clear picture of where the industry is heading. Their software developers and roboticists were coding in Berlin, while the final robot cell was being assembled and deployed in another country by a local team. They never touched the hardware, yet they fully defined the configuration, logic, and performance envelope of a robot cell they would never physically see. Why does this matter? Because it’s the operating model that will dominate automation in the next 3–5 years. You get the right people focused on the right work: - Software teams: Code the logic, optimize performance, test digitally, and push updates. - Mechanical assemblers and electricians: build, wire, and commission the physical cell on-site. You reduce project time. You reduce cost. And you dramatically expand the talent pool by matching skills to tasks instead of forcing every engineer to be a multidisciplinary expert. This separation of duties is only possible with Software-Defined Automation, where every component of the cell is fully software-described, and the complete program can be pushed from the cloud to the edge. To unlock this model, companies need to adopt a unified hardware and software automation platform like the one we pioneered at Vention. When the digital definition matches the physical reality, remote-first automation becomes a reality #SoftwareDefinedAutomation #PhysicalAI #IndustrialAutomation #Robotics

Episode 120 of Machine Minds is live! Robotics is full of obvious opportunities and brutal execution challenges. What slows most teams down is not the robot itself, but the invisible infrastructure underneath it. On this episode, Adrian Macneil, co founder and CEO of Foxglove, joins us to break down why robotics development has lagged behind software, and what it will take to finally change that. Adrian brings deep experience from Coinbase and Cruise, where he helped build the data and developer infrastructure behind early self driving cars. That perspective led him to a clear conclusion: robotics will not scale until foundational tooling becomes off the shelf, interoperable, and built for the realities of physical systems. We cover: • Why robotics development suffers from siloed data, bespoke tooling, and painful debugging • What makes robotics data fundamentally different from traditional software systems • How shared platforms and open standards can unlock faster iteration and more robotics startups If you are building robots, deploying them, or betting on the future of physical AI, this episode dives into the infrastructure layer that actually makes scale possible. Tune in wherever you get your podcasts!

Robotics development is accelerating at an incredible pace. With the release of Kimodo, NVIDIA has taken another major step toward democratizing robot motion generation. Instead of manually defining complex joint trajectories and end-effector paths, the principle is now surprisingly simple: You describe the motion in natural language, and the Kimodo motion model generates the movement. Five years ago, this would have sounded like science fiction. Today, it’s reality! Dr. Pascalis Trentsios explored this topic last weekend and experimented with NVIDIA Kimodo. The results were impressive: motions generated from simple prompts like “a person carrying a heavy package in both arms” or “a person putting a heavy package down on the ground” were translated into realistic motion sequences. Technically, Kimodo uses a motion diffusion model trained on human motion data. The model converts text prompts into motion trajectories, which can then be used in simulation environments or transferred to real robots. This significantly reduces the effort required for motion planning and simulation, and helps close another part of the Sim2Real gap. What we are now seeing in robotics is the same transformation we already see across engineering: Text-to-CAD, Text-to-Simulation, Text-to-Architecture and now Text-to-Motion. Engineers are increasingly freed from low-level technical implementation and can focus more on creative problem solving, system design and innovation. The barriers are falling. The tools are becoming more powerful. The way we engineer systems is changing. What do you think? Will prompt-based engineering become the standard workflow in robotics and engineering? Vlad Larichev | Sebastian Linzmair | Norman Henkel | Nitin Ugale | Dominik Schlicht

The Robot Operating System (ROS) has become the de facto open-source standard for building complex robotics applications (e.g. mobile robots navigating warehouses, robotic arms in manufacturing). Rather than reinventing the wheel, developers can take advantage of ROS’s vast library of pre-built, community-vetted packages for navigation, perception, and motion planning. Its node-based messaging architecture allows systems to be modular, scalable, and adaptable, while simulation and visualization tools like Gazebo and RViz make it possible to test and debug before touching hardware. ROS also benefits from a global open-source ecosystem, bridging education, research, and industry. That said, ROS is not without limitations. Its learning curve can be steep for beginners, and its multi-node design introduces complexity that can be resource-intensive on smaller platforms. While ROS 2 has made great strides with real-time performance and robustness, achieving hard real-time guarantees or running on constrained microcontrollers often requires careful consideration. For simple, single-purpose robots, the overhead of ROS may be unnecessary, and a lightweight framework or custom code might be more efficient. Ultimately, ROS makes the most sense when your project involves multiple sensors, actuators, and intelligent processes that need to work together. In these scenarios, it provides the communication infrastructure and tools to manage that complexity effectively. If you are just getting started with robotics, ROS may be overkill as you work through the basic concepts. When you start working on large, complex robots (or fleets of robots!), having a standardized underlying framework can be crucial. Check out my full blog post to read more about the advantages and disadvantages of ROS: https://lnkd.in/dBpshT7d #ROS #robotics #robot #embedded #programming #software #AI

Physical AI is moving from robots 🤖 to platforms 🧩 CHANGING LIMITS, ACTIVATING FUTURE The recent AGIBot EU roadshow emphasised a simple shift. The demo was a humanoid on stage. The signal was what sits behind it: 🧠 an operating system for embodied AI 🎓 LinkCraft, a locomotion creation platform for motion intelligence 🦾 GeniStudio, an operation development platform for manipulation intelligence 🗣️ LinkSoul, a character and interaction layer for integration intelligence The scaling logic was even clearer: 🔁 teach a skill, capture the data, redeploy faster 🤝 scale through application pilots, plus ODM & OEM partners and developers building on the same hardware platform If you are building or buying Physical AI, the question is no longer “will it work?” It is “how fast can it learn, deploy, and scale?” I wrote more on this in Forbes “Scaling Physical AI: Why Execution Wins” (link in comments) 👇 #PhysicalAI #Robotics #innovation

Software Is Eating Robotics Hardware isn’t where automation’s value grows anymore — software is. Mujin just raised a massive $233 million to expand its AI-driven automation platform. Their core system, MujinOS, blends perception, motion planning, and autonomous control into a single orchestration layer — coordinating robots, conveyors, and sensors across the warehouse floor in real time. This round, led by NTT Group and Qatar Investment Authority, isn’t about more robots. It’s about turning robotics into an intelligent operating system — software that learns, adapts, and optimizes every movement. 💡 Automation ROI will come from integration, not installation. From software-defined control, not hardware scale. Capital is now voting for intelligence over infrastructure — automation is shifting from CapEx to OpEx, from equipment on the floor to intelligence in motion. If your automation strategy still leans hardware-first, you’re solving yesterday’s problem. The next ROI wave lives in the software layer — where orchestration, adaptability, and continuous optimization turn integrators — not installers — into the real winners of this era. 👇 Read this week’s Smart Automation analysis here: https://lnkd.in/e-WRWbEf

The future of robotics will not be built robot-by-robot — it will be deployed like software MahaaAi Group of Companies The next bottleneck in robotics is not hardware — it’s training, deployment, and safe decision-making at scale. At MahaaAi, we are solving this with a governance-driven cognitive architecture + teleportable robotics SaaS model. The Industry Problem Today’s robotics systems face critical limitations: Hundreds of hours of training per environment Simulation-to-reality gaps Lack of decision boundaries between human intent and machine action Safety systems that are reactive, not built-in This makes scaling robotics slow, expensive, and risky. MahaaAi Architecture Solution We are building a Reality-Aware Cognitive Robotics Platform powered by: Scenario-Based Video Simulation Training Train once using real-world scenarios → deploy across environments Teleportable Robotics Intelligence (SaaS Model) AI capabilities are not tied to one robot They can be deployed, transferred, and scaled across fleets instantly Digital Twin + Physics-Aware Learning Simulate before execution Predict outcomes before real-world action Decision Boundary Framework Clear separation between: Human intent → AI reasoning → robotic execution Ensuring controlled autonomy Somavati Engine (Ethical Governance Layer) At the core, MahaaAi integrates the Somavati Engine™: Consent-based intelligence Context-aware behavioral limits No harmful or uncontrolled autonomy Every action is: Explainable. Traceable. Auditable. Business Impact MahaaAi enables: Reduction in training time from months → minutes Faster deployment across industries (agriculture, eldercare, industrial) Safer autonomous systems aligned with human oversight Scalable robotics through platform-based intelligence This is not just robotics. This is a shift from hardware-centric automation → intelligence-driven platforms. We are actively collaborating with global partners, enterprises, and investors to bring teleportable robotics intelligence into real-world deployment. The future of robotics will not be built robot-by-robot — it will be deployed like software. #MahaaAi #Robotics #AIPlatform #DigitalTwin #AutonomousSystems #EthicalAI #DeepTech #SaaS #AIForHumanity

The robotics industry is at a turning point. We're not waiting for better hardware anymore. The real breakthrough is happening in software. Foundation models are changing everything: 🤖 Large behavior models learn whole-body coordination from massive demonstration libraries 🔍 Vision-language-action models translate sensor data into actionable goals 🌍 Open-world models capture environment dynamics for planning ⚡ Generative policies handle uncertainty through diffusion-style denoising These aren't just incremental improvements. They're fundamental shifts. Robots can now generalize beyond their training data. They solve novel problems without task-specific programming. The challenge isn't building better arms or legs. It's teaching machines to think and adapt as we do. Physical AI is moving beyond simple floor cleaners. We're seeing applications in autonomous vehicles, manufacturing, and healthcare. The next breakthrough won't come from a new sensor or actuator. It'll come from smarter algorithms that understand our messy, unpredictable world. This changes how we approach robotics development entirely. What robotics application are you most excited to see benefit from foundation models? #Robotics #AI #FoundationModels