Data Capture Strategies for Robotics Professionals

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.

𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐑𝐨𝐛𝐨𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 - A strong contribution to the robot learning ecosystem is coming out of collaboration between Stanford University, Columbia Engineering, and Toyota - an open-source framework called the Universal Manipulation Interface (UMI) designed to significantly improve how manipulation datasets are collected. One of the major bottlenecks in robot learning has been data acquisition. Conventional pipelines rely heavily on human teleoperation, where operators directly control robot hardware to produce demonstrations.  This process is - - Time-intensive - Hardware-dependent - Expensive to scale - Often limited in visual and interaction richness UMI addresses this bottleneck by introducing a system that enables ~3× faster data collection compared to prior teleoperation-centric approaches, while reducing overall cost and operational complexity. Key technical contributions include - - Decoupled data acquisition interface enabling scalable human demonstration capture - Improved visual context for learning richer state representations - Higher-fidelity action capture, supporting more precise manipulation policies - A pathway toward more scalable policy learning for general-purpose manipulation This is particularly relevant for researchers working in -  - Imitation learning - Visuomotor policy learning - Embodied AI - Generalist robot manipulation systems By lowering the friction in collecting high-quality manipulation data, UMI helps move the field closer to large-scale, diverse datasets — something robotics has historically lacked compared to vision and NLP. Research Paper -   https://lnkd.in/dDdzYmce Open-Source Project -   https://lnkd.in/dnmvcEzh The attached demo video shows the system in action and highlights how interface design can directly influence learning scalability. #education    #elearning     #AI     #datascience

When you're on your last battery set, flying with the precision of a hawk, only to land and discover that no data was collected during the flight😩😩, it can feel like a punch to the gut. 😔 But fear not! Here are some tips to ensure you never encounter this data desert again: 1-Pre-Flight Checklist: Always run through a pre-flight checklist that includes verifying your data collection settings. Make sure your equipment is set up to capture the data you need before you even take off. 2-Real-Time Data Confirmation: If possible, use real-time data monitoring to confirm that your sensors or cameras are actively collecting data as you fly. This immediate feedback loop can save you from an empty dataset. 3-Backup Systems: Consider having a backup system in place. This could be an additional memory card, an extra data logger, or even a secondary device that can capture data in case your primary system fails. 4-Regular Intervals Check: During longer flights, take the time to land and quickly review your data collection at regular intervals. This way, you can catch any issues early on and adjust your settings if needed. 5-Post-Flight Routine: After landing, make it a habit to immediately check your data. This post-flight routine ensures that you're aware of any data collection issues before you pack up and potentially miss the opportunity to recapture lost data. 6-Software Updates: Keep your drone's firmware and any data collection software up to date. Manufacturers often release updates that can improve data collection reliability. 7-Professional Maintenance: Regularly service your equipment with professionals who can identify and fix potential data collection issues before they occur. 8-Training and Education: Stay informed about your equipment's capabilities and any common issues that could affect data collection. The more you know, the better equipped you are to prevent data loss. 9-Remember, every flight is an opportunity to gather valuable information. By implementing these strategies, you can ensure that your data collection is as reliable as your flight skills. 🛫📊 #DataCollection #DroneFlight #PreventativeMeasures #TechTips

In robotics today, I see two common approaches to data collection: 𝟭) 𝗥𝗲𝗰𝗼𝗿𝗱 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗮𝘀𝘆𝗻𝗰𝗵𝗿𝗼𝗻𝗼𝘂𝘀𝗹𝘆 𝗶𝗻𝘁𝗼 𝗠𝗖𝗔𝗣 𝗼𝗿 𝗥𝗢𝗦 𝗯𝗮𝗴𝘀 These formats were designed for robotics debugging and replay, not for data-driven learning.  • They produce massive, unwieldy files. • They store streams as timestamped events, great for replaying an experiment, but terrible for training an imitation learning or reinforcement learning policy, where you need tightly coupled, batched data. • Random access is painful, deserialisation is slow, and converting into ML-ready tensors is a pipeline bottleneck. 𝗜𝗻 𝘀𝗵𝗼𝗿𝘁: MCAP/rosbags are optimised for yesterday’s robotics (ROS 1.0), not for the data-first world of Robotics 2.0 and Physical AI. 𝟮) 𝗦𝘆𝗻𝗰𝗵𝗿𝗼𝗻𝗶𝘀𝗲 𝗱𝗮𝘁𝗮 𝗱𝘂𝗿𝗶𝗻𝗴 𝗿𝗲𝗰𝗼𝗿𝗱𝗶𝗻𝗴 (𝗥𝗢𝗦 + 𝗳𝗶𝘅𝗲𝗱 𝗳𝗿𝗲𝗾𝘂𝗲𝗻𝗰𝘆 𝗹𝗼𝗴𝗴𝗶𝗻𝗴) This is 𝗲𝘃𝗲𝗻 𝘄𝗼𝗿𝘀𝗲. By coupling sensor streams into fixed-frequency snapshots, you:  • Throw away valuable asynchronous signals. • Bake in frequency as a data collection parameter, which is dangerous. • Imagine discovering your policy learns better at 2× the frequency... too late, the information is gone. 𝗕𝗼𝘁𝗵 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵𝗲𝘀 𝗹𝗼𝗰𝗸 𝘆𝗼𝘂 𝗶𝗻𝘁𝗼 𝗿𝗶𝗴𝗶𝗱 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 𝘁𝗵𝗮𝘁 𝗺𝗮𝗸𝗲 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗱𝗮𝘁𝗮-𝗱𝗿𝗶𝘃𝗲𝗻 𝗿𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝘂𝗻𝗻𝗲𝗰𝗲𝘀𝘀𝗮𝗿𝗶𝗹𝘆 𝗽𝗮𝗶𝗻𝗳𝘂𝗹. 𝗔𝘁 𝗡𝗲𝘂𝗿𝗮𝗰𝗼𝗿𝗲, we’ve spent the past decade learning what it takes to store data maximally, in a way that is future-proofed for Robotics 2.0. Our system keeps all raw asynchronous streams, but structures them for efficient, ML-native consumption: random access, sharding, batching, frequency-flexible. Robotics is shifting to a data-first paradigm. If you’re still thinking about data recording as "just hit record", you’re already behind.

How to Scale Data Generation for Physical AI with the NVIDIA Cosmos Cookbook (Become a Reinforcement Learning Robotics Engineer: https://lnkd.in/eFuJ4sxr) 🤖 A Core Challenge in Robotics 🤖 — ⚠️ Robotics models struggle because real-world data is expensive, slow, and often risky to collect — especially when training systems that must understand complex environments, human behavior, lighting variations, object changes, or rare edge cases. 🌐 This data scarcity becomes the biggest bottleneck for perception, navigation, and manipulation models. 🚀 How NVIDIA Cosmos Solves This Data Bottleneck 🚀 🧬 Cosmos world foundation models provide high-fidelity, controllable synthetic data generation at scale, enabling teams to produce diverse, physics-grounded datasets without repeated real-world capture. 🔁 By augmenting existing datasets and generating new ones while maintaining precise structural consistency, Cosmos directly accelerates robotics performance and generalization. 🎨 Cosmos Transfer is world-to-world style transfer model enabling background edits, lighting changes, environmental shifts, and structural-safe video augmentation. 🧩 Multi-control recipes allow developers to generate thousands of realistic variations of the same scene while keeping geometry and motion stable. 🤝 This is critical for robotics workflows, where tasks like gesture recognition, human–robot interaction, or warehouse navigation require robustness across changing environments. 🔧 Control Modalities 🔧 • 🏔️ Depth — Ensures 3D realism and perspective stability. • 🎭 Segmentation — Enables transformation of objects or backgrounds. • ✏️ Edge — Preserves shapes, motion trajectories, and spatial layout. • 🌫️ Vis — Smooths visuals while keeping appearance constant. 🧪 Core Video Recipes 🧪 1️⃣ 🌊 Background Change — Replace environments while preserving motion using filtered_edge + seg + vis. 2️⃣ 💡 Lighting Change — Adapt illumination across conditions via edge + vis. 3️⃣ 🎨 Color & Texture Change — Stable appearance editing through pure edge control. 4️⃣ 📦 Object Change — Modify object classes using balanced edge + seg + vis control weights. 🚀 Sim2Real Data Augmentation for Mobile Robots 🚀 — 🔗 Using Cosmos Transfer with X-Mobility and Mobility Gen, developers generate photorealistic, domain-adapted data that preserves geometry and annotations. 🧭 This is key to bridging the domain gap between Isaac Sim and real-world robotics deployments. 🔬 Technical Highlights 🔬 • 🛠️ Mobility Gen — Creates RGB, depth, and segmentation data for wheeled and legged robots. • 🛰️ X-Mobility — Learns robust navigation models from large-scale diverse datasets. • 🌐 Cosmos Transfer — Multimodal control (edge 0.3, seg 1.0) enables photorealistic Sim2Real adaptation. 🔗 Get started with the Cosmos Cookbook https://lnkd.in/dykt_9sM

Since most Physical AI research is gradually transitioning toward real robots with accurate force sensing, the key question becomes which method to use to scale the data-collection process: teleoperation or kinesthetic teaching? I love both, honestly, but for different reasons. Teleoperation gives you the ability to operate remotely. Kinesthetic teaching, on the other hand, can be trained on the spot and deployed immediately, even in industrial setups, with very low upfront costs. Good: - Teleoperation: Gives you access to remote operators who often come at lower wages and can supervise multiple robots from a single setup behind a screen in a control room. - Kinesthetic teaching (hand guiding): More intuitive because the user is in direct contact with the robot and the object being manipulated. Bad: - Teleoperation: Less intuitive and potentially less accurate due to network delays. Also involves high hardware costs. - Kinesthetic teaching: The user must be physically present with the robot, which can make it harder to factor out human influence in the training dataset. Benefits: - Teleoperation: The same leader device can be used with different robots, including in hazardous environments. - Kinesthetic teaching: Can be retrofitted onto already installed robots and deployed immediately at much lower cost, with significantly lower training costs. Synthetic data through simulation can also be considered a complementary data-collection method, but it falls into another category. It’s great for filling out distributions in bulk, but not for refined, contact-rich data that physics engines still struggle to capture accurately. ...Sometimes I wonder if we need more GPUs or more sensors, tbh... At Bota Systems AG, we enable Physical AI researchers and startups to build high-quality data engines with a sense of touch. Check out more at https://lnkd.in/dKDXADyP

The robotics community has a name for it now: the 100,000-year data gap. You can't scrape robot training data the way you scrape text. It has to be built. And the two options most teams have — teleoperation and hand-authored simulation — are either too expensive to scale or too synthetic to trust at deployment. Here's the part that kept me up at night: Every time a robot hesitates, clips something, or triggers a safety stop in the real world, that's ground-truth data. It's the exact edge case your sim never generated. It has trajectory, context, spatial geometry, failure signature. And in the current workflow, it gets reset and discarded. The failure repeats. The training set stays thin. The sim-to-real gap stays wide. We built Reconstructiv to close that loop. When an incident happens on a real fleet, we detect it, capture the logs and video automatically, and reconstruct the event as a 3D scene — semantically labeled and simulation-ready. The edge case that just happened becomes a training asset before anyone opens a rosbag. Real-world incidents are the most valuable data in robotics. We built the pipeline to stop throwing them away. First look 👇 https://lnkd.in/gZd-M9qB If your team is building VLA or Diffusion Policy models and fighting the data pipeline problem, I'd genuinely love to talk. #PhysicalAI #Robotics #RoboticsML #SimToReal #TrainingData

🤖 Principle 2: Continuous Improvement Relies on Robotics Construction has always relied on experience, instinct, and observation. But in a world where quality is non-negotiable, gut feel isn't enough. Better building starts with better information. And the only way to get it at scale is with robotics. Manual methods are too slow and incomplete to keep up—and they leave quality to chance. Robots like the Dusty FieldPrinter capture valuable information every moment they are being operated on site. This information flows back to project teams and enables closed-loop optimization—empowering teams to solve problems before they escalate and react to field conditions in real time. With real-time visibility into what’s happening on site (or, as the photo suggests, in the air), designers and project managers can respond faster, course-correct earlier, and refine their plans based on what’s actually happening on the ground. And that's just the beginning. As robots become more capable, they can passively gather even more types of field data—from physical measurements to visual discrepancies. That information could automatically flag inconsistencies, raise RFIs, or highlight areas where conditions deviate from the plan—all without waiting for someone to notice. The opportunity to catch issues early and drive better decisions upstream is massive. This kind of passive, high-fidelity data collection opens the door to something powerful: insight that drives action. The information collected on site isn’t just stored—it’s acted on. VDC teams monitor accuracy. Superintendents track progress. Project managers adjust timelines and resource plans. And executives use these patterns to drive smarter decisions across the business. The organizations that engage with this data in real time are the ones that improve the fastest. 📈 Continuous improvement Because when you know exactly: - ✅ What work was performed (and when) - 🔍 Which areas were missed or incomplete - ⏱️ How quickly different sections progressed - ⚠️ Where issues were detected and addressed early You can start making smarter decisions. Faster. And more importantly, you can raise the bar—floor to floor, project to project, across your entire organization. This is how we move from reactive firefighting to proactive quality. This is how we learn, adapt, and build better each time. This is the Dusty Way.

What if 99% of your AI agent training data is not just useless, but actively holding you back? A groundbreaking new paper, "LIMI: Less is More for Agency," provides compelling evidence that the industry's obsession with massive datasets is the wrong path for building truly capable AI agents. The researchers challenged the core assumption of traditional scaling laws ("more data is better"). The results are staggering: - A model trained on just 78 strategically curated examples of complex problem-solving achieved a 53.7% performance gain over models trained on 10,000+ samples. - That's state-of-the-art performance with 128 times LESS data. The secret isn't the data's volume; it's the data's density. Instead of scraping terabytes of text, the researchers captured high-fidelity "trajectories" complete recordings of an expert agent reasoning, using tools, and adapting to feedback to solve complex tasks. It’s the difference between giving an apprentice a library vs. letting them shadow a master craftsperson for a week. The latter is infinitely more valuable for learning a skill. This validates what we’ve been building for at Spacebar.ai. Creating reliable, autonomous agents for complex work isn't a big data problem; it's a systems engineering problem. It requires understanding the essence of a workflow, not just drowning a model in information. This paper proves that mastering agency is about quality, not quantity. For engineering leaders, this research demands a fundamental shift in strategy: 1. Stop Collecting, Start Curating: Your most valuable training data isn't out on the web. It's inside your company, in the actions of your expert users. Invest in tools to capture these expert workflows, not just scrape more documents. 2. Build a "Golden Trajectory" Flywheel: Identify the top 5-10 most complex, value-driving tasks your agents need to perform. Systematically record human-AI collaboration on these tasks to create a small, dense, "golden dataset" for continuous fine-tuning. 3. Treat Data Curation as a Senior Engineering Role: Creating high-quality agentic demonstrations is a design problem, not a data labeling problem. It requires your best engineers and domain experts to craft examples that teach reasoning, tool use, and error correction. 4. Rethink Your MLOps Stack: Is your infrastructure built for processing petabytes of raw data, or for capturing, versioning, and fine-tuning on rich, multi-turn interaction logs? The latter will be the competitive advantage. The age of "Big Data" for AI is giving way to the age of "Deep Data." The winners in the agentic era won't be the ones with the largest datasets, but the ones with the smartest ones. Is your team still chasing scale, or are you focused on curating quality? Link to the paper: https://lnkd.in/eU9RfRAV h/t to Yang Xia, Pengfei Liu, and the entire research team. #AIAgents #AgenticAI #MachineLearning #DataStrategy #LLM #TechLeadership #AI