Intelligent Robotics Systems Development

Build your first robot in simulation! 👾 📌 If you’re self-learning robotics, this is genuinely one of the better repos to save for later. NVIDIA Robotics released a "Getting Started with Isaac Sim" tutorial series covering everything from building your first robot to hardware-in-the-loop deployment. What's inside? → Building Your First Robot Explore the Isaac Sim interface, construct a simple robot model (chassis, wheels, joints), configure physics properties, implement control mechanisms using OmniGraph and ROS 2, integrate sensors (RGB cameras, 2D lidar), and stream sensor data to ROS 2 for real-time visualization in RViz. → Ingesting Robot Assets Import URDF files, prepare simulation environments, add sensors to existing robot models, and access pre-built robots to accelerate development. → Synthetic Data Generation Learn perception models for dynamic robotic tasks, understand synthetic data generation, apply domain randomization with Replicator, generate synthetic datasets, and fine-tune AI perception models with validation. → Software-in-the-Loop (SIL) Build intelligent robots, implement SIL workflows, use OmniGraph for robot control, master Isaac Sim Python scripting, deploy image segmentation with ROS 2 and Isaac ROS, and test with and without simulation. → Hardware-in-the-Loop (HIL) Understand HIL fundamentals, learn NVIDIA Jetson platform, set up the Jetson environment, and deploy Isaac ROS on Jetson hardware. The progression makes sense: start with basics (build a robot), add perception (sensors and data), generate training data (synthetic generation), develop software (SIL), then deploy to hardware (HIL). Each module builds on the previous one. For robotics teams, this is the path to faster iteration. Simulate first, validate in software-in-the-loop, generate synthetic training data at scale, then deploy to hardware with confidence. 🎓 If this helps at least one engineer to become more fluent in the world of robotics, means a lot to me! 🫶🏼 Here's the course (it's free): https://lnkd.in/dRYdkmdi ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com

7 lessons from AirSim: I ran the autonomous systems and robotics research effort at Microsoft for nearly a decade and here are my biggest learnings. Complete blog: https://sca.fo/AAeoC 1. The “PyTorch moment” for robotics needs to come before the “ChatGPT moment”. While there is anticipation towards Foundation Models for robots, scarcity of technical folks well versed in both deep ML and robotics, and a lack of resources for rapid iterations present significant barriers. We need more experts to work on robot and physical intelligence. 2. Most AI workloads on robots can primarily be solved by deep learning. Building robot intelligence requires simultaneously solving a multitude of AI problems, such as perception, state estimation, mapping, planning, control, etc. We are increasingly seeing successes of deep ML across the entire robotics stack. 3. Existing robotic tools are suboptimal for deep ML. Most of the tools originated before the advent of deep ML and cloud and were not designed to address AI. Legacy tools are hard to parallelize on GPU clusters. Infrastructure that is data first, parallelizable, and integrates cloud deeply throughout the robot’s lifecycle is a must. 4. Robotic foundation mosaics + agentic architectures are more likely to deliver than monolithic robot foundation models. The ability to program robots efficiently is one of the most requested use cases and a research area in itself. It currently takes a technical team weeks to program robot behavior. It is clear that foundation mosaics and agentic architecture can deliver huge value now. 5. Cloud + connectivity trumps compute on edge – Yes, even for robotics! Most operator-based robot enterprises either discard or minimally catalog the data due to a lack of data management pipelines and connectivity. Given that robotics is truly a multitasking domain – a robot needs to solve for multiple tasks at once. Connection to the cloud for data management, model refinement, and the ability to make several inference calls simultaneously would be a game changer. 6. Current approaches to robot AI Safety are inadequate Safety research for robotics is at an interesting crossroads. Neurosymbolic representation and analysis is likely an important technique that will enable the application of safety frameworks to robotics. 7. Open source can add to the overhead As a strong advocate for open-source, much of my work has been shared. While open-source offers many benefits, there are a few challenges, especially for robotics, that are less frequently discussed: Robotics is a fragmented and siloed field, and likely initially there will be more users than contributors. Within large orgs, the scope of open-source initiatives may also face limits. AirSim pushed the boundaries of the technology and provided a deep insight into R&D processes. The future of robotics will be built on the principle of being open. Stay tuned as we continue to build @Scafoai

This recent study provides a rigorous framework showing that intelligent behavior in robots cannot be reduced to control algorithms alone. Cognitive capability arises from the joint dynamics of morphology, actuation, sensing, materials, and continuous coupling with the physical environment. The study formalizes how morphology, sensor placement, compliant materials, closed-loop control, and environmental feedback shape perception, planning, and action. It argues that scalable robotic intelligence requires co-design of software and physical structure, not post-hoc adaptation. https://lnkd.in/g8asGqt5 #EmbodiedIntelligence #EmbodiedAI #Robotics #RoboticSystems #MorphologicalComputation #ControlTheory #SensorimotorLoops #Mechatronics #CyberPhysicalSystems #AdaptiveRobotics #SoftRobotics #Perception #Actuation #MaterialsEngineering #DigitalIndustry #Siemens #AIEngineering #WorldModels #RobotLearning

Humanoid robots need to adapt to different tasks, like moving around, handling objects while walking, and working on tables, each requiring a unique way to control the robot’s body. For instance, moving around focuses on tracking how fast the robot's base is moving, while working at a table relies more on controlling the robot's arm movements. Many current methods train robots with specific controls for each task, making it hard for them to switch between tasks smoothly. This new approach suggests using whole-body motion imitation to create a common base that can work for all tasks, helping robots learn general skills that apply to different types of control. With this idea, researchers developed HOVER (Humanoid Versatile Controller), a system that combines different control modes into one shared setup. HOVER allows robots to switch between tasks without losing the strengths needed for each one, making humanoid control easier and more flexible. This approach removes the need to retrain the robot for each task, making it more efficient and adaptable for future uses. The diverse team of researchers that developed HOVER come from: NVIDIA,  Carnegie Mellon University, University of California, Berkeley, The University of Texas at Austin, and UC San Diego. 📝 Research Paper: https://lnkd.in/eMatAxMu 📊 Project Page: https://lnkd.in/eY4gzmme #robotics #research

Robotics is entering a new phase where learning is becoming more autonomous, scalable, and efficient. Instead of relying heavily on large volumes of human-labeled training data, emerging approaches allow robots to learn through simulation, self-exploration, and real-time adaptation. This shift has the potential to significantly reduce development time while improving flexibility across dynamic environments. In practical terms, this means robots can better understand how to interact with unfamiliar objects, refine their movements through trial and feedback, and generalize skills across tasks without being explicitly programmed for each scenario. From manufacturing floors to logistics and even healthcare support, the impact could be substantial. While the progress is promising, it also brings important considerations around reliability, safety, and oversight. As robots gain more independence in how they learn and act, ensuring robust validation and responsible deployment becomes critical. The evolution from data-dependent training to self-directed learning is not just a technical milestone. It represents a broader shift toward more adaptive and intelligent systems that can collaborate with humans more effectively and operate in increasingly complex real-world settings.

What if building a full robotics stack didn’t take a team of ten PhDs… or 2 years of development... or $5 million dollars? That’s exactly what the new Advantech Edge AI SDK is changing. For years, integrating ROS 2 + LiDAR + cameras + AI models has been a heavy lift: - Custom drivers - Sensor fusion pipelines (gPTP anyone?) - SLAM tuning - Model deployment - Endless debugging across disconnected systems It wasn’t just hard—it was resource prohibitive. Now? You can do it in a no-code / low-code environment. ✔ Pull ROS 2 packages directly from GitHub ✔ Drop in LiDAR drivers and camera inputs ✔ Run SLAM out of the box ✔ Load and train AI models for perception ✔ Deploy everything on industrial-grade edge hardware All in one unified stack. This is the real shift: We’re moving from building infrastructure → to building intelligence. Instead of spending months wiring systems together, you can now: Simulate Train Iterate Deploy …in a fraction of the time. My take: Systems built in 1 day with this stack can outperform legacy systems that took 10 years to develop. That’s not incremental progress—that’s a step change. LiDAR users—take note. Sensor fusion, SLAM, and perception are no longer bottlenecks. They’re becoming commoditized (read FREE) capabilities. The competitive edge is shifting to: 👉 How fast you can deploy 👉 How fast you can iterate 👉 How intelligently you use the data The barrier to entry for advanced robotics and physical AI just collapsed. And this is only the beginning. #Advantech #EdgeAI #ROS2 #Robotics #LiDAR #ComputerVision #AMR #AGV #PhysicalAI #Automation #AI #SLAM #IndustrialAI

𝗪𝗵𝗲𝗻 𝗔𝗜 𝗚𝗲𝘁𝘀 𝗮 𝗕𝗼𝗱𝘆 — 𝗧𝗵𝗲 𝗡𝗲𝘅𝘁 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗶𝗻 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 AI is no longer confined to data centers or the cloud. It is entering the physical world, where machines can see, hear, move, and react on their own. This new wave, called 𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗔𝗜, brings intelligence into motion. It powers #robots, #drones and #humanoids that sense, decide, and act in real time. In short, Physical AI gives machines senses, reflexes, and awareness — a body that works with the brain, where #silicon meets motion and algorithms gain instincts. It’s not just about smart code anymore, It is about intelligence that moves. 𝗧𝗵𝗲 𝗕𝗿𝗮𝗶𝗻 & 𝗕𝗼𝗱𝘆 𝗼𝗳 𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗔𝗜 𝟭. 𝗛𝗮𝗿𝗱𝘄𝗮𝗿𝗲 𝗹𝗮𝘆𝗲𝗿 — 𝘁𝗵𝗲 𝘀𝗲𝗻𝘀𝗲𝘀 𝗮𝗻𝗱 𝗺𝘂𝘀𝗰𝗹𝗲𝘀 • 𝗦𝗲𝗻𝘀𝗼𝗿𝘀: give machines sight, sound, and touch through cameras, microphones, LiDAR, and pressure sensors. • 𝗔𝗰𝘁𝘂𝗮𝘁𝗼𝗿𝘀: motors, gears, and brakes that control smooth, precise motion. • 𝗦𝗺𝗮𝗿𝘁 𝗔𝗜 𝗠𝗘𝗠𝗦: tiny chips near sensors enabling instant response with minimal power. 𝟮. 𝗔𝗜 𝗰𝗼𝗺𝗽𝘂𝘁𝗲 𝗹𝗮𝘆𝗲𝗿 — 𝘁𝗵𝗲 𝗯𝗿𝗮𝗶𝗻 • 𝗔𝗜 𝗰𝗵𝗶𝗽𝘀: GPUs, NPUs, and AI SoCs that handle perception, planning, and control directly at the edge. • 𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗶𝗻𝘁𝗲𝗿𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝘀: advanced memory stacks (HBM, LPDDR, GDDR) and fast links (UCIe, CXL, NOC) that move data quickly between sensors, compute, and memory. 𝟯. 𝗦𝘆𝘀𝘁𝗲𝗺 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 — 𝘁𝗵𝗲 𝗻𝗲𝗿𝘃𝗼𝘂𝘀 𝘀𝘆𝘀𝘁𝗲𝗺 • 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗹𝗮𝘆𝗲𝗿𝘀: coordinate how sensors and actuators communicate. • 𝗘𝗺𝗯𝗼𝗱𝗶𝗲𝗱-𝗔𝗜 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀: combine simulation, reinforcement learning, and digital twins. • 𝗖𝗹𝗼𝘂𝗱 𝘂𝗽𝗱𝗮𝘁𝗲𝘀: share experience, safety data, and coordination across fleets. 𝟰. 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 — 𝘁𝗵𝗲 𝗺𝗶𝗻𝗱 • 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹𝘀 𝗳𝗼𝗿 𝗿𝗼𝗯𝗼𝘁𝗶𝗰𝘀: unite vision, language, and motion understanding. • 𝗠𝘂𝗹𝘁𝗶-𝗺𝗼𝗱𝗮𝗹 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴: merge sound, vision, and touch for natural interaction. • 𝗦𝗲𝗹𝗳-𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁𝘀: keep improving as they move and sense the world. 𝗛𝗼𝘄 𝗡𝗲𝘅𝘁-𝗚𝗲𝗻 𝗖𝗵𝗶𝗽𝘀 𝗣𝗼𝘄𝗲𝗿 𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 The future of Physical AI depends on new chips built for the real world, where speed and efficiency both matter. Leaders like NVIDIA, AMD, Intel Corporation, and Qualcomm are shrinking AI into compact, power-aware packages, while startups such as SiMa.ai, Tenstorrent, d-Matrix, BrainChip , Hailo, and EdgeCortix target everything from smart cameras and drones to autonomous robots and vehicles. These #chips use 3D-stacked memory, #chiplet designs, and optimized interconnects to bring intelligence next to the sensors — turning perception into motion almost instantly. Soon, machines will respond as smoothly as living beings, merging awareness and action in a single loop. Murali Chirala Band of Angels Silicon Catalyst Bala Joshi

Agentic AI systems are moving beyond digital environments and into the physical world. We can now see this technology in motion through robotics, autonomous vehicles, and smart infrastructure. How do agents work alongside us in real environments? Our latest AWS Open Source blog explains how teams can build intelligent physical AI systems that bridge edge and cloud computing. By combining Strands Agents SDK, Amazon Bedrock AgentCore, Claude 4.5, NVIDIA GR00T, and Hugging Face LeRobot, customers can create agentic systems that leverage cloud-scale reasoning while maintaining millisecond responsiveness for real-time physical interaction. The architecture enables edge devices to handle fast, instinctual responses while the cloud provides deliberate reasoning and fleet-wide learning. We're seeing remarkable results—from robotic arms performing complex manipulation tasks to autonomous systems that continuously improve through shared experience. Learn about building intelligent physical AI with agentic systems in this deep dive from our team: https://lnkd.in/gEJVuF5F

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

'A roadmap for AI in robotics' - our latest article (https://rdcu.be/euQNq) published in Nature Machine Intelligence, offers an assessment of what artificial intelligence (AI) has achieved for robotics since the 1990s and proposes a research roadmap with challenges and promises. Led by Aude G. Billard, current president of IEEE Robotics and Automation Society, this perspective article discusses the growing excitement around leveraging AI to tackle some of the outstanding barriers to the full deployment of robots in daily lives. It is argued that action and sensing in the physical world pose greater and different challenges for AI than analysing data in isolation and therefore it is important to reflect on which AI approaches are most likely to be successfully applied to robots. Questions to address, among others, are how AI models can be adapted to specific robot designs, tasks and environments. It is argued that for robots to collaborate effectively with humans, they must predict human behaviour without relying on bias-based profiling. Explainability and transparency in AI-driven robot control are essential for building trust, preventing misuse and attributing responsibility in accidents. Finally, the article close with describing the primary long-term challenges, namely, designing robots capable of lifelong learning, and guaranteeing safe deployment and usage, as well as sustainable development. Happy to be co-author of this great piece led by Aude G. Billard, with contributions from Alin Albu-Schaeffer, Michael Beetz, Wolfram Burgard, Peter Corke, Matei Ciocarlie, Danica Kragic, Ken Goldberg, Yukie NAGAI, and Davide Scaramuzza Nature Portfolio IEEE #robotics #robots #ai #artificial #intelligence #sensors #sensation #ann #roadmap #generativeai #learning #perception #edgecomputing #nearsensor #sustainability