标签：#Robot

The contract between the two companies ends in May 2028, Uber told TechCrunch.

US robotics researchers flew to Venezuela with snakebots after getting a call.

The number of paid robotaxi miles traveled fell 36% in the second quarter, despite expanding to new cities, according to Tesla's own figures.

With “blessings” from the original Jibo founders, iKairos is a wearable or desk-mounted “AI journal” that turns your family moments into AI images and video.

Union previously warned automaker that any robot deployment must be negotiated.

Most teams already keep Playwright or Robot Framework specs next to the product. Manual cases often live somewhere else — a spreadsheet, a cloud TMS, or a wiki page nobody updates after the first release. That is why automation coverage is usually a quarterly guess: the catalog and the specs never share an id. Put both in the same Git repo , give every manual case a stable id, and linking becomes a text match. Any IDE agent can do that if the cases are files it can already see — no custom AI product inside a test tool. One repo, two layers of the same suite Manual cases are YAML files under .gitoza-lite/test/cases/ (VS Code / Cursor extension) or .gitoza/test/cases/ (Desktop app). The filename without .yaml is the case id . Automated tests live wherever your team already puts them — tests/ , e2e/ , robot/ — in the same repository. Traceability is a shared id: put that case id on the automation side as a tag (or name), and on the YAML side as automated: true plus a params pointer. A PR can change the case, the Playwright spec, and the link in one review. Step 1 — Draft manual cases in the IDE Open the repo in Cursor or VS Code. Point the agent at a few existing case files so it learns your title style, tags, and step length. Then feed it a user story, a ticket, or a screenshot. Ask for several cases at once, not one shallow step. A useful prompt looks like: Read .gitoza-lite/test/cases/shopflow/auth/ for format. From ticket SHOP-184, draft three YAML cases (happy path, invalid password, locked account). Filename = case id. Use tags auth and smoke where it fits. Save the files under the suite folder. Then open the Gitoza Lite Test Repository tab to browse, edit, and — when you are ready — run them as a manual suite with Pass / Fail / Skip. The extension does not ship its own model. It keeps cases as plain YAML so whatever assistant you already use can read and write them. A minimal case: --- title : Login with valid credentials priority : high tags : [ smoke , auth ]

Ukraine's battlefield surge of robots now features airdrops and beach assaults.

The company didn't say how many are in each city and has taken a far more cautious approach to scaling the network than CEO Elon Musk had promised.

Gritt is coming out of stealth with $34 million and plan to automate the hardest tasks on construction sites.

Agility is opening a new training center for its Digit robots in Fremont, California.

I was tired of buzzword-heavy AI projects and marginally impactful demos. Surely we can do something more inspiring with these LLMs than build another chatbot? For me, the answer is physical AI: the moment all those breakthroughs finally reach into the real world, in robots that see, move, and figure things out for themselves. I think it is the most exciting frontier in tech right now. It is also genuinely hard to break into, because it is not one field. It is about five of them stacked on top of each other: electronics, mechanics, programming, data, and AI. Eight months ago I started my own robotics journey from scratch, and I was completely overwhelmed. How do you get from blinking an LED to a humanoid that does your dishes? There are thousands of scattered tutorials out there, with no sense of what comes first, or what any of it is building toward. So I decided to build the map. Stealing the best idea from my favorite games: If you have ever played a factory-building or strategy game like Satisfactory or Civ Six, you know the feeling. You start with almost nothing, and you unlock new tech one satisfying step at a time. Those games are proof that we will happily spend hours mastering an intimidatingly complex system, as long as it is laid out as a clear tree of unlocks. So why not point that same instinct at learning something real? That is exactly what a tech tree is: a structured, visual path where each node is a skill and each connection is a prerequisite. You start at Curiosity on the far left and work your way right, through electronics, mechanics, code, data, and AI, all the way toward autonomous robots and humanoids. The idea is simple: turn gaming time into learning time. What the tree actually is Every node on the tree is a skill to learn, and the star-shaped nodes are hands-on projects where theory finally meets a soldering iron. Nodes are color-coded by discipline, so you can see at a glance whether you are in electronics, mechanics, programming, data s

The recall comes as the top automotive safety regulator in the U.S. has warned AV companies about their vehicles interfering with first responders.

The CEO of Foundation Future Industries, which counts the president’s son as its chief strategy adviser, tells WIRED it’s exploring some “kinetic things.”

In the wake of a massive hours-long gridlock event, San Francisco mayor Daniel Lurie has told state regulators it's time to put more requirements on robotaxi operators like Waymo.

Hyundai aims to deploy 25,000 Atlas robots starting with US factories in 2028.

​I. The Terminal Velocity of Disembodied Intelligence ​The current paradigm of artificial intelligence is approaching a fundamental cognitive ceiling. We have built vast, sprawling minds, yet we have trapped them in sensory deprivation chambers. Today’s Large Language Models and industrial robotics operate under a fatal flaw: the reliance on linear, arbitrary tokenization. Traditional AI chops raw text into disconnected, non-semantic fragments, wasting immense computational memory tracking the mere phonetic order of spelling and grammar. ​Simultaneously, industrial machines navigate the physical world through siloed sensor pipelines. They process light, space, and kinetics as heavy, raw digital arrays, relying on brute-force algorithmic equations to stitch reality together. This is not intelligence; it is computational exhaustion. To achieve true artificial general intelligence, the machine must stop reading about the world and begin to inhabit it. ​II. The Biological Imperative ​Nature solved the hardware-software integration problem millions of years ago. Human biology remains the ultimate benchmark for flawless, metabolic efficiency. Our somatosensory system does not wait for a central brain to read a text string before pulling a hand away from a fire; it processes physical feedback in milliseconds through decentralized neural highways. ​Human cognition leverages cross-modal integration within the angular gyrus, allowing visual data to be instantly cross-referenced with kinesthetic feedback to trigger anticipatory motor reflexes. Furthermore, biological intelligence is inherently tied to physical survival. We manage thermodynamics through a centralized hypothalamic thermostat, protecting the body from cellular destruction, while routing ambient light data to deep-brain structures to drive a natural circadian rhythm. True AGI requires this exact synthesis of abstract thought and biological mechanics. ​III. The Logographic Paradigm Shift ​To bridge the gap between

If you have ever built an autonomous mobile robot, you have likely run into the dreaded "Shuffled USB Port" problem. You boot up your robot, fire up your ROS 2 launch files, and... crash. Your LiDAR driver is trying to parse data from your IMU, and your IMU node is screaming about invalid serial frames. Because Linux assigns virtual serial paths like /dev/ttyUSB0 and /dev/ttyUSB1 based purely on which device initialized milliseconds faster, relying on default OS paths is a recipe for system instability. When you are scaling up to dozens of Jetson Orin nodes —each equipped with an RPLIDAR C1 and a Yahboom 10-axis IMU —manually hardcoding paths or writing rigid scripts on every individual machine isn't viable. Here is how production-grade robotics fleets handle plug-and-play USB binding dynamically using configuration-driven udev rules. The Core Concept: Vendor ID vs. Physical Port vs. Serials Linux's udev (device manager) allows us to dynamically create stable symbolic links (symlinks) like /dev/rplidar and /dev/imu when hardware is plugged in. How we identify those devices determines our fleet's flexibility: USB Serials: Unique to each individual chip. Highly secure, but requires registering every single replacement sensor in your codebase. Physical USB Ports ( KERNELS ): Tied to a physical slot on the carrier board. Great if you have identical sensors, but forces technicians to plug cables into highly specific, undocumented ports. Vendor ID (VID) & Product ID (PID): Identifies the USB-to-serial converter chip on the sensor board. Because the RPLIDAR C1 uses a Silicon Labs CP210x chip ( 10c4:ea60 ) and the Yahboom IMU uses a QinHeng CH340 chip ( 1a86:7523 ), they use completely distinct silicon. This means we can map them dynamically and reliably using just their VID/PID —allowing field technicians to plug them into any USB port on the Jetson without breaking the system. Step 1: The Configuration-Driven File ( devices.conf ) Hardcoding vendor rules inside shell scri

Boston Dynamics' robotic quadruped Spot has already found work doing routine factory inspections and patrolling the ruins of Pompeii, but what about deliveries? The company is testing a new conveyor belt accessory that allows Spot to carry packages from a vehicle and autonomously unload them on a customer's doorstep in an effort to reduce a […]

The company is raising at least $75 million, led by Robot Ventures, with significant participation from USV and other prominent investors.

Washington D.C. has become a battleground for Uber and Waymo's competing views.