Strategies to Advance Next-Generation Robotics

The world of Robotics just changed overnight. And it’s been in the works for years. I still remember how excited we were when NVIDIA’s Jensen Huang gave a unique shout-out to Gideon at #GTC21 (check out the video below), hinting at what would eventually happen when the worlds of AI and Robotics collide. Jensen back then: "The signs are clear: accelerated computing doing AI at data center scale will give a giant boost in simulation performance." Jensen today: "Everything that moves in the future will be robotic." NVIDIA Robotics just announced a series of robotics breakthroughs at NVIDIA GTC, with a clear aim of democratizing the building of AI Robots with game-changing foundational components and tools: • Isaac Manipulator, a collection of state-of-the-art motion generation and modular AI capabilities for robotic arms, • Isaac Perceptor, Visual AI for Autonomous Mobile Robot (watch out if you’re building smart AMRs!), • GR00T, a general-purpose foundation model for humanoid robot learning, • a new Jetson Thor-based computer for humanoid robots, built on the NVIDIA Thor SoC, • Isaac Lab for robot learning, • Isaac OSMO for hybrid-cloud workflow orchestration. Mindblowing. 😮 It validates what we at Gideon have believed in for the past 7 years: the future of flexible robots will be powered by advanced visual perception and AI. If you want to build meaningful robotics companies, there’s never been a better time. And it’s never been more important to: 1. Listen to your early customers and focus on adding value to them from day one. Build long-term relationships with their People and help them solve their top problems. 2. Specialize! Focus on solving one specific problem at a time. Do not build universal platforms, trying to tackle many problems at once. When customers hear about your company, they should immediately know you’re the best in the world to solve a specific problem they have. 3. Do not reinvent the wheel; use the off-the-shelf components whenever possible. 4. Data to train your robots is key. Generalized components and platforms will always miss industry-specific data and customer insights you should have access to, so use them to build. It’s your secret superpower and a future growth flywheel. 5. Make sure your robots talk to and cooperate well with other systems. 6. Do not underestimate the complexities of deploying AI robots in the real world, especially in commercial environments. Invest in people, processes, and tools to handle this properly early on. This will make or break you. The real world is nothing like your simulation environment. 7. Partner with key industry players to accelerate your growth (like we did with Toyota Material Handling Europe.) All the building blocks are finally coming together. What is the robot you’ll start working on today? #NVIDIA #JensenHuang #Robotics #AI #AIRobotics #VisualAI #VisualPerception #ComputerVision #GTC24 #AMR #AGV #MobileRobots #HumanoidRobots

Reindustrializing #Europe in the age of AI 🤖”—our latest report outlines what it will take: Amid intensifying global competition in AI and #Robotics, Europe faces a defining moment: reindustrialize or risk falling irreversibly behind. Robotics can help restore industrial sovereignty, address demographic headwinds, and boost productivity. We propose a 5-point strategic roadmap to reposition Europe as a credible competitor alongside the US and China: 1️⃣ A European Robotics Roadmap – Focus on building champions in high-impact, under-robotized sectors: logistics, hospitality, agrifood, healthcare, aerospace, and defense. Prioritize strategic autonomy, not chasing lost ground in humanoids or autonomous vehicles. 2️⃣ Capital Access for Robotics Startups – Address the 7x VC funding gap with the US by scaling Europe’s venture capital market and reinforcing complementary funding streams. 3️⃣ Bridging Innovation and Market – Tackle fragmentation through innovation clusters, regional champions, and greater public-private investment coordination. We recommend increasing the 2028–2034 EU budget by at least 5% with a dedicated robotics allocation. 4️⃣ Upskilling the Workforce – Tackle skill shortages across factory floors and engineering teams. From frontline operators to system integrators, we need a unified "Robot Skills Framework" and modern vocational training. 5️⃣ Smart Regulation – Align AI and robotics regulation to promote innovation. Use regulatory sandboxes, harmonized safety standards, and dynamic, risk-based approaches to support adoption—especially among SMEs. 📘 Download the full report: https://lnkd.in/evxEPDgn #Robotics #AI #IndustrialPolicy #Reindustrialization #Innovation #VentureCapital #FutureOfWork #TechSovereignty #Automation #Manufacturing #Ludonomics #AllianzTrade #Allianz

HOT OFF THE PRESS: Australia's National Robotics Strategy (launched today) presents a roadmap for integrating advanced robotics and automation technologies to boost national productivity and global competitiveness. This strategic vision is particularly impactful for WA and the mining sector, where advancements in autonomous and semi-autonomous systems are poised to significantly enhance operational safety and efficiency. At the Australian Automation and Robotics Precinct, where I serve as the Safety Lead, I am privileged to get a front row seat for this transformative journey. The precinct is actively collaborating with innovators like Jevons Robotics with their ARTEV 6000 automated remote terrain vehicle and IMDEX, whose semi-autonomous Blast Dog system exemplifies the practical benefits of robotics in mining, improving safety and operational precision. With my roles leading safety at the precinct, educator in technology and responsible innovation at the Safety Innovation Academy and through my consulting at Pocketknife Group; I have a personal mission to drive the sustainable and responsible adoption of these technologies, ensuring they contribute positively to workplace safety and operational practices. Key aspects of the Australian Robotics Strategy focus on enhancing national capabilities in robotics, promoting wider adoption across industries, fostering responsible development with an emphasis on trust and inclusion, and enriching skills and diversity within the robotics workforce. For health and safety professionals, particularly those working in high-hazard industries, the strategic deployment of robotics and automation promises reduced exposure to hazardous work environments and a transformation in traditional safety practices - we can lead the way. Download the strategy here: https://lnkd.in/g6Eg8Ub9 #SafetyTech #SafetyInnovation #Robotics #Automation #SafetyTechnology #SafetyTechNews #DigitalSafetyStrategy #SafetyTransformation

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

Robotics is no longer about hardware — it’s about who owns the data. When RealMan Robotics opened its data training center in Beijing, it wasn’t just creating another research hub — it was signaling the industry’s shift from hardware-driven innovation to data-driven performance. For years, robotics progress centered on building faster autonomous mobile robots (AMRs), stronger arms, and more precise sensors. But performance is hitting diminishing returns. The real bottleneck? Training data. Robots generate massive sensory streams, but without curated datasets, algorithms don’t improve. That’s why structured data pipelines have become the “conveyor belts” of the AI era. We’re seeing this across industries and geographies: • ROVR Network’s open dataset is democratizing access, much like ImageNet did for computer vision. • NVIDIA and Qualcomm are embedding compute at the edge, ensuring robots learn from local feedback in real time. • Siemens’ Shanghai plant reported a 350% efficiency leap — not from better robots, but from data-driven orchestration across fleets. • John Deere’s acquisition of GUSS Automation signals that agricultural robotics is following the same path: the value is in data loops, not just machines. The strategic play is clear: robotics differentiation is moving from hardware to data-driven adaptability. For operators, this means three things: • Data becomes the moat. Running robots today train the models that will dominate tomorrow. • Partnerships will shift. Expect joint ventures between OEMs, logistics providers, and manufacturers to pool datasets. • Competitive advantage compounds. Companies that build proprietary feedback loops will accelerate past those who just buy off-the-shelf robots. The critical question for leaders: are you just automating tasks—or building the dataset that future-proofs your operation? I put together the most comprehensive weekly newsletter on automation, robotics and innovation driving intralogistics, supply chains, and e-commerce. ↓ https://lnkd.in/evrCrS2P

Robotics is entering a turning point - and reinforcement learning may be at the heart of it. Traditional methods teach robots with fixed rewards (“do X correctly, get Y point”). This works for simple tasks but breaks down in messy, real-world scenarios. That’s where Group Relative Policy Optimization comes in. Instead of giving a single reward, GRPO compares multiple possible outcomes of a task and ranks them. Think of it like a robot trying several approaches, then learning which was “less bad” and iterating from there - much closer to how humans refine skills. The promise is big: robots that adapt more flexibly to navigation, manipulation, or even multi robot collaboration. But there’s a catch. GRPO needs lots of trial and error runs. In simulation, this is cheap. In the real world, it’s expensive, time consuming, and sometimes unsafe. GRPO doesn’t magically solve robotics however. It does give us a training signal that looks more like how people learn - by comparing alternatives - and it scales. If we pair that with high‑throughput simulation and disciplined sim‑to‑real practice, the next wave of robot learning will look less like clever reward hacking and more like robust skill acquisition. #innovation #technology #future #management #startups

'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