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What does it look like when a robot truly understands where it is in the real world? It starts with Niantic Spatial’s Scaniverse, capturing a real-world environment and reconstructing it into a high-fidelity Gaussian splat. From there, our Robot Adventures demo simulates how VPS could localize a robot within... show more

Niantic Spatial 🌎

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20,350 次观看 • 4 个月前 •via X (Twitter)

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A Gaussian Splat can become a world where Robots and AI agents can act. In our latest OVER Research experiment, we placed a robot inside a real-world 3D capture, with a VLM making decisions based on what it sees. At every step, the robot holds a pose in the reconstruction, gets a newly rendered view of the environment, takes an action, moves, and sees the world again from its new position. Why does this matter? Because 3D captures can become more than reconstructions to explore. They can become environments where embodied AI and robots can navigate, act, be evaluated and eventually train across real-world spaces at scale. Capture a place once. Then turn it into a world where AI and Robots can act. The full experiment, including what we discovered once we actually put the loop to the test:

A Gaussian Splat can become a world where Robots and AI agents can act. In our latest OVER Research experiment, we placed a robot inside a real-world 3D capture, with a VLM making decisions based on what it sees. At every step, the robot holds a pose in the reconstruction, gets a newly rendered view of the environment, takes an action, moves, and sees the world again from its new position. Why does this matter? Because 3D captures can become more than reconstructions to explore. They can become environments where embodied AI and robots can navigate, act, be evaluated and eventually train across real-world spaces at scale. Capture a place once. Then turn it into a world where AI and Robots can act. The full experiment, including what we discovered once we actually put the loop to the test:

Over the Reality 🌐

14,975 次观看 • 22 天前

Most AI-generated worlds are optimized to look realistic. But physical AI systems operating in the real world need something much harder: geometry that is physically consistent, spatially accurate, and grounded to real-world coordinates. Our latest Ground Truth blog breaks down how Niantic Spatial reconstructs georeferenced 3D environments from off-the-shelf drones, phones, and 360° imagery — solving depth, alignment, and surface consistency at scale. 🔗Read more: #NianticSpatial #Spexi #PhysicalAI #Reconstruction #GaussianSplat #DigitalTwins #Robotics #GeospatialAI #AI #GeospatialIntelligence #GEOINT #SpatialIntelligence

Most AI-generated worlds are optimized to look realistic. But physical AI systems operating in the real world need something much harder: geometry that is physically consistent, spatially accurate, and grounded to real-world coordinates. Our latest Ground Truth blog breaks down how Niantic Spatial reconstructs georeferenced 3D environments from off-the-shelf drones, phones, and 360° imagery — solving depth, alignment, and surface consistency at scale. 🔗Read more: #NianticSpatial #Spexi #PhysicalAI #Reconstruction #GaussianSplat #DigitalTwins #Robotics #GeospatialAI #AI #GeospatialIntelligence #GEOINT #SpatialIntelligence

Niantic Spatial 🌎

22,832 次观看 • 3 个月前

From seeing → to understanding → to following → now to finding. In the last update, our robot learned how to recognize and follow people SR Agentic now evolves from human-following → to goal-driven object search. Give it a task like: → “find the white bottle on the table ” It will: • break down the instruction via LLM task planning • scan the environment in real-time • localize the object in 3D space • navigate and approach autonomously Step by step, capability by capability — from perception → to tracking → to task execution in the physical world. This is how real-world agentic robotics is built

From seeing → to understanding → to following → now to finding. In the last update, our robot learned how to recognize and follow people SR Agentic now evolves from human-following → to goal-driven object search. Give it a task like: → “find the white bottle on the table ” It will: • break down the instruction via LLM task planning • scan the environment in real-time • localize the object in 3D space • navigate and approach autonomously Step by step, capability by capability — from perception → to tracking → to task execution in the physical world. This is how real-world agentic robotics is built

Strike Robot

22,038 次观看 • 5 个月前

The next manipulation tool may not live on your screen. It may stand in front of you look into your eyes and convince you that it understands. Would you rather face a humanoid robot strong enough to hurt you or one designed well enough to make you trust it? This robot copies blinking, eye contact, head movement and facial expressions to create the illusion of human presence. That may look impressive but it also opens a darker question. When a machine can look concerned appear friendly and imitate emotion people may start trusting signals that contain no real feeling no empathy and no moral responsibility. So which is more dangerous a robot with physical power or a robot that can manufacture trust? #HumanoidRobot #Robotics #AI

The next manipulation tool may not live on your screen. It may stand in front of you look into your eyes and convince you that it understands. Would you rather face a humanoid robot strong enough to hurt you or one designed well enough to make you trust it? This robot copies blinking, eye contact, head movement and facial expressions to create the illusion of human presence. That may look impressive but it also opens a darker question. When a machine can look concerned appear friendly and imitate emotion people may start trusting signals that contain no real feeling no empathy and no moral responsibility. So which is more dangerous a robot with physical power or a robot that can manufacture trust? #HumanoidRobot #Robotics #AI

Techniahqrobot | humanoid robots

11,650 次观看 • 2 个月前

A robot just parked and docked a Citi Bike by itself in the middle of NYC. 🤖🚲 We’re entering the era where AI won’t just think… it will interact with the real world beside us. 👀 #AI #Robotics #IoT #5G #Tech #innovation #SmartCities

A robot just parked and docked a Citi Bike by itself in the middle of NYC. 🤖🚲 We’re entering the era where AI won’t just think… it will interact with the real world beside us. 👀 #AI #Robotics #IoT #5G #Tech #innovation #SmartCities

Fabrizio Bustamante Escudero

65,393 次观看 • 4 个月前

This is how Strike Robot turns Simulation into Reality! One of the biggest challenges in robotics is ensuring that behaviors validated in simulation work reliably in the real world. For this experiment, we reconstructed part of a real laboratory at Eastworlds inside SR Platform. The generated layout was then deployed into MuJoCo. Using SR Agentic, the robot was tasked with finding abnormal objects in a cluttered environment and sending a Telegram notification when detected. Before deployment, everything is validated in simulation.

This is how Strike Robot turns Simulation into Reality! One of the biggest challenges in robotics is ensuring that behaviors validated in simulation work reliably in the real world. For this experiment, we reconstructed part of a real laboratory at Eastworlds inside SR Platform. The generated layout was then deployed into MuJoCo. Using SR Agentic, the robot was tasked with finding abnormal objects in a cluttered environment and sending a Telegram notification when detected. Before deployment, everything is validated in simulation.

Strike Robot

15,350 次观看 • 3 个月前

Hey #NeuraxonMini is literally out! , we manage to "transplant" a Neuraxon 2 bioinspired #AI brain to a physical robot the #SpheroMini moving from our last Scientific Paper (link bellow) by David Vivancos - e/acc & Jose Sánchez for Qubic #OpenScience hybridized with #Aigarth to the real World. First you need a Sphero Education Mini robot about 50$ Then you can try the first cool demos at Hugging Face: 1.- Neuraxon2MiniControl to drive the sphero robot 2.- Neuraxon2MiniWrite to write letters or words with physical moves of the sphero robot using Neuraxon Video Tutorials on youtube later today. Why this matters? Remember we are not building "dead" LLMs we are building #AliveAIs and for that we need to explore how it behaves in reality, from how it learns to how it fails, and what better way that in the emerging field of #robotics , time will tell if your next #HumanoidRobot have a #Neuraxon brain... Read the Paper: Explore the Neuraxon code here: Are you ready for #TrueAI ?

Hey #NeuraxonMini is literally out! , we manage to "transplant" a Neuraxon 2 bioinspired #AI brain to a physical robot the #SpheroMini moving from our last Scientific Paper (link bellow) by David Vivancos - e/acc & Jose Sánchez for Qubic #OpenScience hybridized with #Aigarth to the real World. First you need a Sphero Education Mini robot about 50$ Then you can try the first cool demos at Hugging Face: 1.- Neuraxon2MiniControl to drive the sphero robot 2.- Neuraxon2MiniWrite to write letters or words with physical moves of the sphero robot using Neuraxon Video Tutorials on youtube later today. Why this matters? Remember we are not building "dead" LLMs we are building #AliveAIs and for that we need to explore how it behaves in reality, from how it learns to how it fails, and what better way that in the emerging field of #robotics , time will tell if your next #HumanoidRobot have a #Neuraxon brain... Read the Paper: Explore the Neuraxon code here: Are you ready for #TrueAI ?

David Vivancos - e/acc

29,293 次观看 • 7 个月前

Yeah! Teleoperation on low-cost robot arm from Tau Robotics working natively at 200Hz with and a very simple code. We are working a video tutorial to make it easy for anyone to reproduce our setup (in simulation or with a real robot). Stay tuned 😋

Yeah! Teleoperation on low-cost robot arm from Tau Robotics working natively at 200Hz with and a very simple code. We are working a video tutorial to make it easy for anyone to reproduce our setup (in simulation or with a real robot). Stay tuned 😋

Remi Cadene

31,497 次观看 • 2 年前

High school students built an autonomous ball-collecting robot! 🎾 A group of high school students built a robot that picks up balls and shoots them into a bin while moving without stopping, with impressive speed and accuracy. It combines mechanical design, sensors, and software making constant adjustments in real time while the robot is driving. When teenagers can build systems this sophisticated, the talent pipeline for the robotics industry is accelerating! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

High school students built an autonomous ball-collecting robot! 🎾 A group of high school students built a robot that picks up balls and shoots them into a bin while moving without stopping, with impressive speed and accuracy. It combines mechanical design, sensors, and software making constant adjustments in real time while the robot is driving. When teenagers can build systems this sophisticated, the talent pipeline for the robotics industry is accelerating! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

1,200,467 次观看 • 6 个月前

A HUMANOID ROBOT IS ON THE RUN. THE POLICE CANNOT CATCH IT. The highway is blocked. Police cruisers and an ambulance are following behind. Ahead of them, a humanoid medical robot keeps running without slowing down or getting tired. A few years ago, this would have looked like a scene from a sci-fi movie. Today, it looks like an ordinary live news broadcast. This is 100% AI-generated. But what if this chase were real? How would you react to a robot the police could not catch?

A HUMANOID ROBOT IS ON THE RUN. THE POLICE CANNOT CATCH IT. The highway is blocked. Police cruisers and an ambulance are following behind. Ahead of them, a humanoid medical robot keeps running without slowing down or getting tired. A few years ago, this would have looked like a scene from a sci-fi movie. Today, it looks like an ordinary live news broadcast. This is 100% AI-generated. But what if this chase were real? How would you react to a robot the police could not catch?

CHQ

22,291 次观看 • 9 天前

From seeing → to understanding → now to following. In the previous video, we showed how the robot sees and navigates the world. This time, we’ve upgraded SR Agentic — it can now identify a designated person and autonomously follow them in real-time. Powered by perception + reasoning + adaptive navigation, the robot doesn’t just react - it tracks intent and moves with purpose.

From seeing → to understanding → now to following. In the previous video, we showed how the robot sees and navigates the world. This time, we’ve upgraded SR Agentic — it can now identify a designated person and autonomously follow them in real-time. Powered by perception + reasoning + adaptive navigation, the robot doesn’t just react - it tracks intent and moves with purpose.

Strike Robot

25,478 次观看 • 5 个月前

JUST IN: Reimagine Robotics has just emerged from stealth! 🥷🏻 Its approach to robot training is one of the most human-centric I've seen. The founder is Jonathan Scholz, the person who built and led Google DeepMind's Applied Robotics team in London for seven years. This is not a first-time founder taking a swing at robotics. He has spent a decade at the frontier of the field. The philosophy is powerful. He calls it "monkey-see, monkey-do." 🐒 A worker shows the robot what to do. Watches it attempt the task. Corrects it on the spot. The robot learns. No specialist programmers. No months of integration. And it's already working in the real world: → A made-to-order plastics business trained robots to tend 3D printers overnight, removing print beds, operating latches, pressing controls → A hard drive disassembly facility built a three-robot cell combining robots and people to recover critical materials → Time to prototype and test a new robot behaviour reduced from one day to 10 MINUTES That last number is the one that changes everything. When testing a new behaviour takes 10 minutes instead of a day, the entire pace of deployment transforms. Scholz's framing of the human-robot relationship is worth reading carefully: "A robot that learns on the job depends on people. The worker identifies the bottleneck, shows the robot how to help, and corrects it until it is useful." It's August, and we keep getting robotics bangers week in week. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

JUST IN: Reimagine Robotics has just emerged from stealth! 🥷🏻 Its approach to robot training is one of the most human-centric I've seen. The founder is Jonathan Scholz, the person who built and led Google DeepMind's Applied Robotics team in London for seven years. This is not a first-time founder taking a swing at robotics. He has spent a decade at the frontier of the field. The philosophy is powerful. He calls it "monkey-see, monkey-do." 🐒 A worker shows the robot what to do. Watches it attempt the task. Corrects it on the spot. The robot learns. No specialist programmers. No months of integration. And it's already working in the real world: → A made-to-order plastics business trained robots to tend 3D printers overnight, removing print beds, operating latches, pressing controls → A hard drive disassembly facility built a three-robot cell combining robots and people to recover critical materials → Time to prototype and test a new robot behaviour reduced from one day to 10 MINUTES That last number is the one that changes everything. When testing a new behaviour takes 10 minutes instead of a day, the entire pace of deployment transforms. Scholz's framing of the human-robot relationship is worth reading carefully: "A robot that learns on the job depends on people. The worker identifies the bottleneck, shows the robot how to help, and corrects it until it is useful." It's August, and we keep getting robotics bangers week in week. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

23,389 次观看 • 1 个月前

Train a TensorFlow object detection model – then deploy it on a robot 🤖 Iulia Feroli (Iulia Feroli) shows how to turn a notebook into a real-time object detection app. This tutorial works for any project – though we demonstrate the deployment on (and assisted by!) the #ReachyMini, an open-source robot from Pollen Robotics. Built with PyCharm + Claude Code. 👉 Watch it in action:

Train a TensorFlow object detection model – then deploy it on a robot 🤖 Iulia Feroli (Iulia Feroli) shows how to turn a notebook into a real-time object detection app. This tutorial works for any project – though we demonstrate the deployment on (and assisted by!) the #ReachyMini, an open-source robot from Pollen Robotics. Built with PyCharm + Claude Code. 👉 Watch it in action:

PyCharm, a JetBrains IDE

34,908 次观看 • 4 个月前

🚨 THIS IS HOW IT STARTS — HUMANOID ROBOT SUDDENLY LUNGES AT A MAN — EXACTLY THE MOMENT PEOPLE FEARED A humanoid robot was casually kicking a soccer ball around during a public demonstration. Then the ball rolled toward a man in the audience. He tried to kick it back. That’s when the robot suddenly charged toward him and raised its arm like it was about to swing. Within seconds, a handler rushed in and grabbed the robot before the moment escalated. People in the room laugh nervously… but the clip is making the rounds online for one reason: The movement looks almost human. The way it runs. The stance. The arm coming up. Even the comments are losing it: • “Tf was it going to HIT for???” • “Why did it need to be held back?” • “DON’T TOUCH MY BALL” • “How do they get pissed off if they supposedly don’t have emotions?” The strangest part? The robot seemed to react the instant the human interfered with the ball. Are we just watching a glitch during a demo… or the exact moment people warned about?

🚨 THIS IS HOW IT STARTS — HUMANOID ROBOT SUDDENLY LUNGES AT A MAN — EXACTLY THE MOMENT PEOPLE FEARED A humanoid robot was casually kicking a soccer ball around during a public demonstration. Then the ball rolled toward a man in the audience. He tried to kick it back. That’s when the robot suddenly charged toward him and raised its arm like it was about to swing. Within seconds, a handler rushed in and grabbed the robot before the moment escalated. People in the room laugh nervously… but the clip is making the rounds online for one reason: The movement looks almost human. The way it runs. The stance. The arm coming up. Even the comments are losing it: • “Tf was it going to HIT for???” • “Why did it need to be held back?” • “DON’T TOUCH MY BALL” • “How do they get pissed off if they supposedly don’t have emotions?” The strangest part? The robot seemed to react the instant the human interfered with the ball. Are we just watching a glitch during a demo… or the exact moment people warned about?

HustleBitch

54,191 次观看 • 6 个月前

Live from World Robot Conference 2026 in Beijing: ROBOTERA is showcasing teleoperation of the XHAND 1 Pro dexterous hand with MANUS™ gloves. Video 1: a ROBOTERA operator demonstrates high-precision teleoperation. Video 2: a visitor tries the setup without first running a user-specific calibration, yet the teleoperation remains stable, smooth, and responsive. With 21 active degrees of freedom, fully direct-drive actuation, and integrated tactile sensing, XHAND 1 Pro is built for advanced robotic manipulation and embodied intelligence research. MANUS provides high-fidelity, low-latency hand motion data as real-time input for robot control. #WRC2026 #robotics #humanoids #teleoperation #PhysicalAI

Live from World Robot Conference 2026 in Beijing: ROBOTERA is showcasing teleoperation of the XHAND 1 Pro dexterous hand with MANUS™ gloves. Video 1: a ROBOTERA operator demonstrates high-precision teleoperation. Video 2: a visitor tries the setup without first running a user-specific calibration, yet the teleoperation remains stable, smooth, and responsive. With 21 active degrees of freedom, fully direct-drive actuation, and integrated tactile sensing, XHAND 1 Pro is built for advanced robotic manipulation and embodied intelligence research. MANUS provides high-fidelity, low-latency hand motion data as real-time input for robot control. #WRC2026 #robotics #humanoids #teleoperation #PhysicalAI

MANUS™

11,998 次观看 • 1 个月前

Bring new robot testing environments to life with World Labs and Isaac Sim. 🤖 If you can describe a world 🌎, you can start testing in it the same day. Learn how to: 1. Export scenes from World Labs' Marble as Gaussian splats 2. Convert to USD using NVIDIA Omniverse NuRec 3. Import into NVIDIA Isaac Sim 4. Add a robot and run the simulation Read the guide ➡️ #SIGGRAPHAsia2025

Bring new robot testing environments to life with World Labs and Isaac Sim. 🤖 If you can describe a world 🌎, you can start testing in it the same day. Learn how to: 1. Export scenes from World Labs' Marble as Gaussian splats 2. Convert to USD using NVIDIA Omniverse NuRec 3. Import into NVIDIA Isaac Sim 4. Add a robot and run the simulation Read the guide ➡️ #SIGGRAPHAsia2025

NVIDIA Robotics

158,102 次观看 • 9 个月前

I'll never forget how many people didn't understand what I was doing with this proof of concept, and cried that it was impossible for a robot quadruped to handle recoil, and stay on target. Look where we are today. Ghost Robotics vision 60 on the range!

I'll never forget how many people didn't understand what I was doing with this proof of concept, and cried that it was impossible for a robot quadruped to handle recoil, and stay on target. Look where we are today. Ghost Robotics vision 60 on the range!

KF

1,162,941 次观看 • 2 个月前

Memo is a robot that uses AI to perform household tasks effectively. Today Sunday announced its Series B, and we’re proud to be investors. Training robots for the home is hard — the environment is messy, dynamic, and full of edge cases. So Sunday is training robots directly on real households. Founders Tony Zhao and Cheng Chi built a glove-based system that lets hundreds of contributors record everyday tasks in their own homes, creating high-fidelity demonstrations that feed directly into robot learning. Home robotics will be defined by the companies that learn fastest from real homes. Sunday is building that loop. More here: Aaref Hilaly Amanda Huang

Memo is a robot that uses AI to perform household tasks effectively. Today Sunday announced its Series B, and we’re proud to be investors. Training robots for the home is hard — the environment is messy, dynamic, and full of edge cases. So Sunday is training robots directly on real households. Founders Tony Zhao and Cheng Chi built a glove-based system that lets hundreds of contributors record everyday tasks in their own homes, creating high-fidelity demonstrations that feed directly into robot learning. Home robotics will be defined by the companies that learn fastest from real homes. Sunday is building that loop. More here: Aaref Hilaly Amanda Huang

Bain Capital Ventures

22,024 次观看 • 6 个月前