Advancing Robotics Technology

Researchers at the City University of Hong Kong have developed miniature, caterpillar-like robots that might change the way we deliver medications and perform surgeries inside the human body. What Are These Millirobots? - Biodegradable and Soft: Made from a gelatin-like material combined with iron oxide microparticles, these tiny robots are about the size of a fingernail. Their soft composition allows them to move through the body without harming delicate tissues. - Magnetic Control: The iron oxide particles make the robots responsive to external magnetic fields. This means doctors can further control the direction their movement precisely, guiding them to specific locations within the body. - Inspired by Insects: Mimicking the walking and gripping abilities of caterpillars, these robots can roll, fold, and even grasp small objects with their claw-like appendages. This flexibility enables them to navigate complex internal environments like the gastrointestinal tract. How Do They Work? The robots can be coated with medications. Once guided to the target area, they unfold their bodies to release the drug directly where it's needed, potentially increasing the treatment's effectiveness and reducing side effects. Their ability to grasp and transport objects opens up possibilities for performing surgical tasks without the need for large incisions or invasive instruments. After completing their mission, the robots naturally break down over a few days into harmless substances, eliminating the need for surgical retrieval. While still in the experimental phase, these tiny robots have shown promise in laboratory tests. The researchers successfully guided them through a model of the gastrointestinal system, demonstrating their potential for real-world medical use. Would you be comfortable with such technology being used in medical treatments? #innovation #technology #future #management #startups

Today, Science Robotics has published our work on the first drone performing fully #neuromorphic vision and control for autonomous flight! 🥳 Deep neural networks have led to amazing progress in Artificial Intelligence and promise to be a game-changer as well for autonomous robots 🤖. A major challenge is that the computing hardware for running deep neural networks can still be quite heavy and power consuming. This is particularly problematic for small robots like lightweight drones, for which most deep nets are currently out of reach. A new type of neuromorphic hardware draws inspiration from the efficiency of animal eyes 👁 and brains 🧠. Neuromorphic cameras do not record images at a fixed frame rate, but instead have the pixels track the brightness over time, sending a signal only when the brightness changes. These signals can now be sent to a neuromorphic processor, in which the neurons communicate with each other via binary spikes, simplifying calculations. The resulting asynchronous, sparse sensing and processing promises to be both quick and energy efficient! 🔋 In our article, we investigated how a spiking neural network (#SNN) can be trained and deployed on a neuromorphic processor for perceiving and controlling drone flight 🚁. Specifically, we split the network in two. First, we trained an SNN to transform the signals from a downward looking neuromorphic camera to estimates of the drone’s own motion. This network was trained on data coming from our drone itself, with self-supervised learning. Second, we used an artificial evolution 🦠🐒🚶♂️ to train another SNN for controlling a simulated drone. This network transformed the simulated drone’s motion into motor commands such as the drone’s orientation. We then merged the two SNNs 👩🏻🤝👩🏻 and deployed the resulting network on Intel Labs’ neuromorphic research chip "Loihi". The merged network immediately worked on the drone, successfully bridging the reality gap. Moreover, the results highlight the promises of neuromorphic sensing and processing: The network ran 10-64x faster 🏎💨 than a comparable network on a traditional embedded GPU and used 3x less energy. I want to first congratulate all co-authors at TU Delft | Aerospace Engineering: Federico Paredes Vallés, Jesse Hagenaars, Julien Dupeyroux, Stein Stroobants, and Yingfu Xu 🎉 Moreover, I would like to thank the Intel Labs' Neuromorphic Computing Lab and the Intel Neuromorphic Research Community (#INRC) for their support with Loihi (among others Mike Davies and Yulia Sandamirskaya). Finally, I would like to thank NWO (Dutch Research Council), the Air Force Office of Scientific Research (AFOSR) and Office of Naval Research Global (ONR Global) for funding this project. All relevant links can be found below. Delft University of Technology, Science Magazine #neuromorphic #spiking #SNN #spikingneuralnetworks #drones #AI #robotics #robot #opticalflow #control #realitygap

Terminator just went open source. This humanoid robot you’re seeing isn’t being manually programmed. It’s being controlled by an AI agent. Over the past few years we’ve watched AI slowly expand its reach. First it was text. Then images. Then video and voice. Then AI started operating software and doing tasks on computers... Now it’s starting to control physical machines. A developer called Stash Pomichter and the team behind Dimensional / DimOS just released an open-source system that connects AI agents like OpenClaw directly to robots. Humanoids, drones, quadrupeds, robotic arms. The idea is simple but powerful. Instead of writing thousands of lines of robotics code, you can prompt the system in natural language and the agent handles perception, navigation, memory, and control. Cameras, lidar, spatial mapping, movement, planning. Basically the robotics version of vibe coding. You tell the robot what you want and the agent figures out how to do it. In their demo a Unitree humanoid robot runs through this stack and can be instructed with a simple command while the agent interprets the environment and decides how to act. They also built something called Spatial Agent Memory, which lets the AI store and search real-world information over time. Things like where objects were seen, who entered a room, how spaces are structured, and what happened earlier. So the agent isn’t just reacting. It’s building a model of the physical world. And the entire stack was just released open source. If social media suddenly filled with AI-generated text, images, and videos once the tools became accessible… imagine what happens when physical robots become programmable the same way. What a time to be alive. Follow Endrit Restelica for more tech stuff.

Not long ago, solving a Rubik’s Cube was considered a mark of human intelligence and spatial reasoning. Can you solve the Cube that fast? Today, AI-powered robots can do it in 0.103 seconds, thanks to ultra-fast cameras capturing 4,500 frames per second and motors executing rotations in under 10 milliseconds. It’s more than a party trick — it’s a signal of how far robotics and AI have come. 📈 Processing Power: Since 2010, compute performance for AI workloads has grown by over 1 million×. ⚙️ Robotics Precision: Modern servomotors can reach accuracy levels below 5 microns, enabling surgical precision. 🧠 Learning Efficiency: Reinforcement learning models can now train 10× faster using GPU and accelerator platforms like AMD Instinct and ROCm. 🌐 Adoption Rate: Over 70% of manufacturers are investing in autonomous robotics or cobots to boost productivity and safety. The Rubik’s Cube isn’t the story — it’s the metaphor. Machines have evolved from replicating human logic to outpacing it, not through brute force but through speed, adaptability, and self-optimization. 🔹 Robots that invent their own challenges to learn faster. 🔹 AI systems that design and test hardware in simulation before humans even prototype it. 🔹 Collaborative robotics that co-create with humans — blending creativity, empathy, and logic. AI and robotics are no longer about automation; they’re about amplifying imagination. #AI #Robotics #Innovation via @cuberx5w #MachineLearning #FutureTech #Automation #ReinforcementLearning

VLA models are systems that combine three capabilities into one framework: seeing the world through cameras, understanding natural language instructions like "pick up the red apple," and generating the actual motor commands to make a robot do it. Before these unified models existed, robots had separate modules for vision, language, and movement that were stitched together with manual engineering, which made them brittle and unable to handle new situations. This review paper covers over 80 VLA models published in the past three years, organizing them into a taxonomy based on their architectures—some use a single end-to-end network, others separate high-level planning from low-level control, some use diffusion models for smoother action sequences. The paper walks through how these models are trained using both internet data and robot demonstration datasets, then maps out where they're being applied. The later sections lay out the concrete technical problems that remain unsolved. Read online with an AI tutor: https://lnkd.in/eZdzYfdu PDF: https://lnkd.in/ezzncewE

Understanding user intent is foundational to improving any AI-driven product experience. In this tech blog, Udemy’s engineering team shares how they evolved their intent-understanding system by incorporating LLMs, ultimately improving the user experience of the Udemy AI Assistant. - For the Assistant to work well, the very first step is figuring out what a learner actually means so that the system can take the right action. Early versions relied on a lightweight sentence-embedding model: user messages were mapped to a vector space and matched against example utterances to identify the closest intent. This approach worked reasonably well at the start, but as the Assistant grew to support more features and nuanced intents, it began to struggle, leading to more misclassifications and weaker responses. - To improve accuracy, the team explored larger embedding models and eventually tested using LLMs directly for intent classification. While this LLM-only approach significantly improved understanding by leveraging full conversational context, it also came with higher latency and cost. The key was a hybrid strategy: use embeddings when confidence is high, and fall back to a smaller LLM only when intent is ambiguous. This delivered a strong balance between accuracy and efficiency in production. What stands out is how real-world constraints shaped the final design. In production systems, there are always trade-offs between quality, speed, and cost—and the “best” architecture is rarely the most complex one. Udemy’s approach is a useful reminder that combining lightweight methods with LLMs in the right places can meaningfully improve user experience without over-engineering the solution. #DataScience #MachineLearning #LLM #ProductAI #AppliedML #MLSystems #IntentUnderstanding #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/ga5JJuzN

Stop trying to build one massive AI agent. You're setting yourself up for hallucinations and latency spikes. Here are 5 architectural patterns that separate fragile demos from robust, production systems. ⬇️ I see too many teams struggle because they treat agent development like advanced prompt engineering. It's not just about prompts—it's about architecture. The 'just chat with it' phase is over. Building production-grade agents requires real engineering. 1. Decomposing Workflows Break down complex tasks into smaller, specialized agents. Have a 'supervisor' agent route requests to the right specialist—one for understanding user intent, another for retrieving data, a third for complex reasoning. This approach simplifies maintenance and makes scaling much easier. 2. Future-Proofing Your Architecture The complex logic you build today could become a single API call tomorrow as models improve. The field is moving incredibly fast. Design your system in a modular way, so you can easily swap out custom components when a better, native solution becomes available. 3. Embedding Multimodality Text-only is no longer enough. The best agent systems are built with multimodality from day one. They can process user images, understand visual context, and even generate visual outputs. Don't treat it as an add-on; it's fundamental for a complete and accurate solution. 4. Leveraging Open Protocols Stop wasting engineering cycles on custom API wrappers. Adopt open standards for both agent-to-agent (A2A) and agent-to-tool communication (MCP). This allows your decomposed agents (see point #1) to collaborate seamlessly and lets them dynamically discover and use tools with a standardized format. You're building a scalable ecosystem, not a maintenance nightmare of fragile, custom integrations. 5. Separating Reasoning & Execution Never let an LLM perform calculations or write directly to a database. That's a critical mistake. Use the LLM for what it's good at: reasoning and understanding intent. Then, force its output into a strict format (like a Pydantic model), validate it, and pass it to reliable, deterministic code for the actual execution. Let the LLM think, let your code do. Building reliable agents is a serious engineering challenge. Respect the fundamentals. What's the biggest architectural lesson you've learned building AI agents? ♻️ Repost this if you find it useful. 🔔 Follow me for more on production AI. #AgenticAI #MLOps #EnterpriseArchitecture #AIStrategy

Singapore Hotel Association learning journey in Shenzhen and Hong Kong: on physical robots. I just came back from an excellent journey on the topic of innovation and sustainability in hospitality. Shenzhen, in China, is just opposite to Hong Kong and is part of what is called "the Greater Bay Area" ecosystem, hosting thousands of manufacturing companies there. But don't be mistaken: we are not talking about 20th century old plants. We are talking about high tech manufacturing. The city has much more modern infrastructures than most cities in the world. The robotics fields is doing impressive progress. For hotels, security and cleaning robots for public spaces are completely common, delivery robots from reception to guest rooms as well (especially for economy to midscale hotels) - some solutions start to emerge to allow for brownfield integration without full change of lifts. Housekeeping robots - the dream of hoteliers! - are not ready yet but it is coming fast, see the video in this post: the startup currently showcasing cleaning robots for bathrooms and toilets was just launched 9 months ago! It shows the speed at how these companies move. Human like robots are now capable of impressive balance and movements, even better than most of us, but they are not yet autonomous to recognize the world around them: this is the next wave, enabled with GenAI embedded software which will act as the brain of the robots, able to converse with us and manipulate the physical world. This is already the case in warehouse, logistics where the environment is simple, and I expect that in 2-3 years in China we will see this in hotels - A lot of inspiration to get from hotels in Singapore and elsewhere. #Hospitality #Innovation #China #Technology #Robotics #TheWayForward

The construction industry is undergoing a major shift—driven by robotics and AI. In the Middle East, where ambitious projects and rapid urban growth define the landscape, these technologies offer real solutions to labor shortages, safety concerns, and sustainability goals. Robotic systems like autonomous excavators, 3D printing robots, and AI-powered survey tools are revolutionizing how we design and build. They enhance speed, precision, and safety—while reducing waste and enabling eco-friendly construction. The UAE is uniquely positioned to lead this transformation. With national strategies focused on innovation and net-zero goals by 2050, robotics can play a vital role in shaping a more sustainable and resilient built environment. Success stories, like ACCIONA’s use of robotic layout printers in Portugal, demonstrate how the region can integrate cutting-edge solutions into large-scale infrastructure. It’s time to embrace robotics, not just to build faster, but to build smarter, greener, and better.

A surgical milestone - rewriting the rules of the operating room. In a groundbreaking study by Johns Hopkins University, a robot named SRT-H performed a gallbladder removal surgery entirely on its own - no human hands, no pre-programmed steps, just real-time decisions and surgical finesse. 🔍 What makes this different? The robot was trained using videos of human surgeons performing procedures on pig cadavers, paired with natural language captions to teach step-by-step tasks. It completed 17 complex surgical steps with 100% accuracy across eight trials, adapting to anatomical variations and even recovering from visual obstructions like blood-like dyes. Built on the same machine learning architecture as ChatGPT, it responded to spoken commands like “grab the gallbladder head” and corrected its own movements mid-surgery. 💡 Why it matters: This is a leap from robots that follow rigid plans to ones that understand and adapt like junior surgical residents. It opens doors to autonomous procedures in rural clinics, emergency settings, and resource-limited environments. The robot’s performance was comparable to expert surgeons, though slightly slower - showing promise for precision without fatigue. #roboticsurgery #medicainnovation #sciencerobotics