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While Anthropic and OpenAI race on AI Agents/LLMs, Mistral continues expanding its footprint in physical AI. They just released Robostral Navigate (8B VLA model). It shows strong potential in delivery, logistics, manufacturing, and hospitality--for example, enabling humanoid robots to autonomously navigate multiple rooms to hand a glass of water... show more

CyberRobo

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10,704 次观看 • 6 天前 •via X (Twitter)

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Mistral AI Releases Robostral Navigate: An 8B Model Enabling Robots to Navigate Complex Environments Hitting 76.6% on R2R-CE With One RGB Camera. No LiDAR. No depth sensor. No multi-camera rig. Here's how it works. 👇 1. Pointing, not metric commands The model predicts the pixel coordinates of the next target in the camera view, plus the arrival orientation. Working in pixel space keeps it robust to camera intrinsics and world scale. When the target leaves the frame, it falls back to local displacements ("2m forward, 1.5m left, turn 25°"). 2. Grounding-first No open-source VLM base. It starts from Mistral's grounding model (pointing, counting, localization). Navigation emerges once the model knows where things are. → ~400,000 trajectories across 6,000 simulated scenes 3. Prefix-caching for training A tree-based attention mask packs a full episode into one sequence — all time steps in a single forward pass. → 22× fewer training tokens; months of training done in days 4. Online RL on top After supervised training, CISPO adds trial-and-error learning to fight distribution shift from behavior cloning. → +3.2% success rate from RL alone 5. The numbers (R2R-CE, Matterport3D) → 76.6% success on validation unseen → +9.7 pts over best single-camera approach → +4.5 pts over best depth/multi-camera system The key takeaway: state-of-the-art continuous VLN without a sensor stack — grounding-init, pixel-space actions, prefix-cached SFT, and online RL, on one RGB camera. Full analysis: Technical details: Mistral AI Mistral AI for Developers

Mistral AI Releases Robostral Navigate: An 8B Model Enabling Robots to Navigate Complex Environments Hitting 76.6% on R2R-CE With One RGB Camera. No LiDAR. No depth sensor. No multi-camera rig. Here's how it works. 👇 1. Pointing, not metric commands The model predicts the pixel coordinates of the next target in the camera view, plus the arrival orientation. Working in pixel space keeps it robust to camera intrinsics and world scale. When the target leaves the frame, it falls back to local displacements ("2m forward, 1.5m left, turn 25°"). 2. Grounding-first No open-source VLM base. It starts from Mistral's grounding model (pointing, counting, localization). Navigation emerges once the model knows where things are. → ~400,000 trajectories across 6,000 simulated scenes 3. Prefix-caching for training A tree-based attention mask packs a full episode into one sequence — all time steps in a single forward pass. → 22× fewer training tokens; months of training done in days 4. Online RL on top After supervised training, CISPO adds trial-and-error learning to fight distribution shift from behavior cloning. → +3.2% success rate from RL alone 5. The numbers (R2R-CE, Matterport3D) → 76.6% success on validation unseen → +9.7 pts over best single-camera approach → +4.5 pts over best depth/multi-camera system The key takeaway: state-of-the-art continuous VLN without a sensor stack — grounding-init, pixel-space actions, prefix-cached SFT, and online RL, on one RGB camera. Full analysis: Technical details: Mistral AI Mistral AI for Developers

Marktechpost AI

39,955 次观看 • 4 天前

Wow. That's cool. Researchers just released World, an open-source Unreal Engine 5 simulator for training and testing LLM and VLM agents in realistic 3D environments. The platform supports RGB, depth, and segmentation sensors, along with navigation, vehicles, pedestrians, robots and procedural city generation. It's built with a Gym-like Python interface, it allows AI agents to learn physical and social reasoning in complex virtual worlds before real world deployment. We're moving from AI that only understands information to AI that can perceive, reason and act inside realistic simulated environments.

Wow. That's cool. Researchers just released World, an open-source Unreal Engine 5 simulator for training and testing LLM and VLM agents in realistic 3D environments. The platform supports RGB, depth, and segmentation sensors, along with navigation, vehicles, pedestrians, robots and procedural city generation. It's built with a Gym-like Python interface, it allows AI agents to learn physical and social reasoning in complex virtual worlds before real world deployment. We're moving from AI that only understands information to AI that can perceive, reason and act inside realistic simulated environments.

SciTech Era

47,401 次观看 • 1 个月前

In just ~3 months, as a solo founder with no prior robotics experience, General Trajectory trained a foundation model for dexterous manipulation that lets humanoid robots pick up unseen objects and perform real-world work. It generalizes to novel objects and scenes, including cases where prior SoTA models achieve 0% success. Congrats on the launch Joshua!

In just ~3 months, as a solo founder with no prior robotics experience, General Trajectory trained a foundation model for dexterous manipulation that lets humanoid robots pick up unseen objects and perform real-world work. It generalizes to novel objects and scenes, including cases where prior SoTA models achieve 0% success. Congrats on the launch Joshua!

Y Combinator

68,605 次观看 • 6 个月前

Humanoid robots are cool. But most of them aren’t ready for real-world work. X-Humanoid just launched Embodied Tien Kung 3.0 - a full-size humanoid robot built to actually operate in industrial and commercial environments. What makes it different? • Strong, high-torque joints for heavy tasks • Can move dynamically and stay stable • Handles complex motions (even over obstacles) • Millimeter-level precision for detailed work It runs on the Wise KaiWu AI platform, which allows the robot to see, think, and act in a continuous loop with less human control and support for multi-robot collaboration. It also supports ROS2, MQTT, and TCP/IP, and offers more open hardware + software interfaces to make development easier for teams. The goal is simple: make humanoid robots practical, easier to build on, and ready for real-world use. Visit: Check out the official launch video below to see what it can do! 👇 #Robotics #HumanoidRobot #AI

Humanoid robots are cool. But most of them aren’t ready for real-world work. X-Humanoid just launched Embodied Tien Kung 3.0 - a full-size humanoid robot built to actually operate in industrial and commercial environments. What makes it different? • Strong, high-torque joints for heavy tasks • Can move dynamically and stay stable • Handles complex motions (even over obstacles) • Millimeter-level precision for detailed work It runs on the Wise KaiWu AI platform, which allows the robot to see, think, and act in a continuous loop with less human control and support for multi-robot collaboration. It also supports ROS2, MQTT, and TCP/IP, and offers more open hardware + software interfaces to make development easier for teams. The goal is simple: make humanoid robots practical, easier to build on, and ready for real-world use. Visit: Check out the official launch video below to see what it can do! 👇 #Robotics #HumanoidRobot #AI

Chidanand Tripathi

69,604 次观看 • 4 个月前

Excited to announce GR00T N1, the world’s first open foundation model for humanoid robots! We are on a mission to democratize Physical AI. The power of general robot brain, in the palm of your hand - with only 2B parameters, N1 learns from the most diverse physical action dataset ever compiled and punches above its weight: - Real humanoid teleoperation data. - Large-scale simulation data: we are open-sourcing 300K+ trajectories! - Neural trajectories: we apply SOTA video generation models to “hallucinate” new synthetic data that features accurate physics in pixels. Using Jensen’s words, “systematically infinite data”! - Latent actions: we develop novel algorithms to extract action tokens from in-the-wild human videos and neural generated videos. GR00T N1 is a single end-to-end neural net, from photons to actions: - Vision-Language Model (System 2) that interprets the physical world through vision and language instructions, enabling robots to reason about their environment and instructions, and plan the right actions. - Diffusion Transformer (System 1) that “renders” smooth and precise motor actions at 120 Hz, executing the latent plan made by System 2. We deploy N1 on GR1 robot, 1X Neo robot, and a large collection of simulation benchmarks. N1 achieves up to +30% boost in diverse manipulation tasks for household and industrial settings. While humanoid robots are the main focus of N1, our model also supports cross-embodiment. We finetune it to work on the $110 HuggingFace LeRobot SO100 robot arm! Open robot brain runs on open hardware. Sounds just right. Let’s solve robotics, together, one token at a time. Links to our Whitepaper, Github repo, HuggingFace model, and open dataset page in the thread: 🧵

Excited to announce GR00T N1, the world’s first open foundation model for humanoid robots! We are on a mission to democratize Physical AI. The power of general robot brain, in the palm of your hand - with only 2B parameters, N1 learns from the most diverse physical action dataset ever compiled and punches above its weight: - Real humanoid teleoperation data. - Large-scale simulation data: we are open-sourcing 300K+ trajectories! - Neural trajectories: we apply SOTA video generation models to “hallucinate” new synthetic data that features accurate physics in pixels. Using Jensen’s words, “systematically infinite data”! - Latent actions: we develop novel algorithms to extract action tokens from in-the-wild human videos and neural generated videos. GR00T N1 is a single end-to-end neural net, from photons to actions: - Vision-Language Model (System 2) that interprets the physical world through vision and language instructions, enabling robots to reason about their environment and instructions, and plan the right actions. - Diffusion Transformer (System 1) that “renders” smooth and precise motor actions at 120 Hz, executing the latent plan made by System 2. We deploy N1 on GR1 robot, 1X Neo robot, and a large collection of simulation benchmarks. N1 achieves up to +30% boost in diverse manipulation tasks for household and industrial settings. While humanoid robots are the main focus of N1, our model also supports cross-embodiment. We finetune it to work on the $110 HuggingFace LeRobot SO100 robot arm! Open robot brain runs on open hardware. Sounds just right. Let’s solve robotics, together, one token at a time. Links to our Whitepaper, Github repo, HuggingFace model, and open dataset page in the thread: 🧵

Jim Fan

465,968 次观看 • 1 年前

🚨🇨🇳 World’s first end-to-end robotic hotel launching in China The hotel will use robots for all seven key operations - check-in, luggage, room service, food & beverage delivery, cleaning, security, and guest escort. 🔸 The robots run as one unified team on a single platform 🔸 They coordinate actions and make decisions completely autonomously, no humans needed 🔸 The complex is scheduled to welcome its first tourists in 2027 🔸 By that moment, it aims to also automate landing, sightseeing, accommodation, and shopping China is leaving the world's tourism industry in the dust with its technological edge.

🚨🇨🇳 World’s first end-to-end robotic hotel launching in China The hotel will use robots for all seven key operations - check-in, luggage, room service, food & beverage delivery, cleaning, security, and guest escort. 🔸 The robots run as one unified team on a single platform 🔸 They coordinate actions and make decisions completely autonomously, no humans needed 🔸 The complex is scheduled to welcome its first tourists in 2027 🔸 By that moment, it aims to also automate landing, sightseeing, accommodation, and shopping China is leaving the world's tourism industry in the dust with its technological edge.

Sputnik

23,291 次观看 • 1 个月前

🚨 Apple joins the robotics race! Researchers from Carnegie Mellon University and Apple have developed a robot collision avoidance system. They called it ARMOR and they implemented it on the Fourier GR-1 robot. What is ARMOR? 👘 ARMOR is a system that helps humanoid robots "see" their surroundings better by using small, smart depth sensors placed on their arms. It solves the problem of blind spots and occlusions in traditional robot perception, making robots much safer and more efficient. It uses Time-of-Flight (ToF) sensors to detect obstacles, providing a near-complete view of the robot’s environment. 🔭 A smart software powered by transformer-based AI learns from human movements to help robots plan their actions dynamically. ARMOR reduces collisions by an impressive 63.7%, making robots significantly better at navigating crowded or complex spaces. The system is fast as well! 💨 ARMOR-Policy cuts computational time by 26× compared to traditional planning methods, ensuring robots can act quickly. P.S. Thoughts on this? The research paper is in the comments! ~~~ ♻️ RT to help 1 robot find a new workplace.

🚨 Apple joins the robotics race! Researchers from Carnegie Mellon University and Apple have developed a robot collision avoidance system. They called it ARMOR and they implemented it on the Fourier GR-1 robot. What is ARMOR? 👘 ARMOR is a system that helps humanoid robots "see" their surroundings better by using small, smart depth sensors placed on their arms. It solves the problem of blind spots and occlusions in traditional robot perception, making robots much safer and more efficient. It uses Time-of-Flight (ToF) sensors to detect obstacles, providing a near-complete view of the robot’s environment. 🔭 A smart software powered by transformer-based AI learns from human movements to help robots plan their actions dynamically. ARMOR reduces collisions by an impressive 63.7%, making robots significantly better at navigating crowded or complex spaces. The system is fast as well! 💨 ARMOR-Policy cuts computational time by 26× compared to traditional planning methods, ensuring robots can act quickly. P.S. Thoughts on this? The research paper is in the comments! ~~~ ♻️ RT to help 1 robot find a new workplace.

Lukas Ziegler

169,472 次观看 • 1 年前

The world’s first industrial superhumanoid robot? Dexterity has released Mech - a superhumanoid, powered by Physical AI, to operate in complex, unstructured environments across industrial sites. Unlike traditional robots, Mech moves with the skill of a human but has the strength and stamina of a machine, making it a powerful tool for tough industrial jobs. As industries push for greater efficiency and safety, could superhumanoids like Mech redefine the future of industrial work?

The world’s first industrial superhumanoid robot? Dexterity has released Mech - a superhumanoid, powered by Physical AI, to operate in complex, unstructured environments across industrial sites. Unlike traditional robots, Mech moves with the skill of a human but has the strength and stamina of a machine, making it a powerful tool for tough industrial jobs. As industries push for greater efficiency and safety, could superhumanoids like Mech redefine the future of industrial work?

Circuit

85,351 次观看 • 1 年前

MIT engineers built an AI wristband that controls robots by reading your hand muscles. It works by using an ultrasound to capture images of the muscles and tendons in your wrist. An AI algorithm then translates those images into the exact position of all 5 fingers, tracking 22 different ways your hand can bend, twist, and extend. In demos, wearers directed a robotic hand to play piano just by moving their own fingers. To put the complexity in perspective, it takes: > 34 muscles > 27 joints > 100+ tendons ...Just to scroll your phone This device captures it all from a band the size of a smartwatch. They tested across 8 volunteers with different hand sizes, tracking everything from sign language letters to holding scissors. The team is now collecting data from hundreds more users to train robots in surgical procedures. Something like this could one day become the universal remote for robots and virtual worlds, and we're way closer than most people expect.

MIT engineers built an AI wristband that controls robots by reading your hand muscles. It works by using an ultrasound to capture images of the muscles and tendons in your wrist. An AI algorithm then translates those images into the exact position of all 5 fingers, tracking 22 different ways your hand can bend, twist, and extend. In demos, wearers directed a robotic hand to play piano just by moving their own fingers. To put the complexity in perspective, it takes: > 34 muscles > 27 joints > 100+ tendons ...Just to scroll your phone This device captures it all from a band the size of a smartwatch. They tested across 8 volunteers with different hand sizes, tracking everything from sign language letters to holding scissors. The team is now collecting data from hundreds more users to train robots in surgical procedures. Something like this could one day become the universal remote for robots and virtual worlds, and we're way closer than most people expect.

Rowan Cheung

27,999 次观看 • 3 个月前

SkildAI, which is developing a foundational AI model for robots, has released a new video of their humanoid robot walking over objects and exploring environments it had never seen before. The robot is using vision & adapts in real time. SkildAI: "From raw images and joint feedback, the model directly outputs low-level motor commands. This single neural network enables humanoid robots to seamlessly walk across flat ground, climb stairs, and step over obstacles without any planning, mapping, or manual switching between behaviors. For all of our testing in this video, we deployed the robot in each new environment without any prior planning or mapping. We took off the humanoid’s shoes so you can hear it.

SkildAI, which is developing a foundational AI model for robots, has released a new video of their humanoid robot walking over objects and exploring environments it had never seen before. The robot is using vision & adapts in real time. SkildAI: "From raw images and joint feedback, the model directly outputs low-level motor commands. This single neural network enables humanoid robots to seamlessly walk across flat ground, climb stairs, and step over obstacles without any planning, mapping, or manual switching between behaviors. For all of our testing in this video, we deployed the robot in each new environment without any prior planning or mapping. We took off the humanoid’s shoes so you can hear it.

Sawyer Merritt

69,297 次观看 • 11 个月前

The robot’s “eyes” just received a big upgrade. LingBot-Depth 2.0, a depth-completion model with half the depth error just dropped. 12/16 benchmarks topped. Glass, mirrors, and transparent objects are so easy for us humans, but so hard for robots, because they do not behave like ordinary surfaces in a camera pipeline. A robot that misunderstands a balcony window or a table edge, will have a completely false planning inside a false world. Huge implecation. LingBot-Depth 2.0 takes an RGB image plus a broken depth map from a sensor and then outputs a cleaner depth map and a usable 3D point cloud. Numbers on LingBot-Depth 2.0 - Excels on glass, mirrors & transparent objects — where traditional depth cameras fail - Training data: 3M → 150M (50x scale-up) - 12 out of 16 first-place rankings on depth completion benchmarks - RMSE cut in half: 0.132 → 0.062 on the hardest indoor scenes LingBot-Vision trained on boundaries, because object edges carry the geometry robots need. No human boundary labels are used, which makes this approach easier to scale. The open-sourced LingBot-Vision is the general vision backbone, and LingBot-Depth 2.0 is the depth model built on it.

The robot’s “eyes” just received a big upgrade. LingBot-Depth 2.0, a depth-completion model with half the depth error just dropped. 12/16 benchmarks topped. Glass, mirrors, and transparent objects are so easy for us humans, but so hard for robots, because they do not behave like ordinary surfaces in a camera pipeline. A robot that misunderstands a balcony window or a table edge, will have a completely false planning inside a false world. Huge implecation. LingBot-Depth 2.0 takes an RGB image plus a broken depth map from a sensor and then outputs a cleaner depth map and a usable 3D point cloud. Numbers on LingBot-Depth 2.0 - Excels on glass, mirrors & transparent objects — where traditional depth cameras fail - Training data: 3M → 150M (50x scale-up) - 12 out of 16 first-place rankings on depth completion benchmarks - RMSE cut in half: 0.132 → 0.062 on the hardest indoor scenes LingBot-Vision trained on boundaries, because object edges carry the geometry robots need. No human boundary labels are used, which makes this approach easier to scale. The open-sourced LingBot-Vision is the general vision backbone, and LingBot-Depth 2.0 is the depth model built on it.

Rohan Paul

25,312 次观看 • 12 天前

Japan Shimizu Corporation is testing humanoid robots on live construction sites. Start with basic patrols… An upgraded Unitree H1 humanoid, equipped with a handheld camera, is autonomously patrolling the Torch Tower site at 1.0 m/s. It perceives the environment in real time, follows designated routes, and they plan to use multimodal LLMs to analyze footage for better site management. Long-term, Shimizu aims to build a construction-focused Physical AI ecosystem--covering data collection, simulation, model training, and deployment. The goal is to transfer skilled workers’ expertise to humanoids and tackle labor shortages. the first real step toward physical AGI is simply stepping into the real world.

Japan Shimizu Corporation is testing humanoid robots on live construction sites. Start with basic patrols… An upgraded Unitree H1 humanoid, equipped with a handheld camera, is autonomously patrolling the Torch Tower site at 1.0 m/s. It perceives the environment in real time, follows designated routes, and they plan to use multimodal LLMs to analyze footage for better site management. Long-term, Shimizu aims to build a construction-focused Physical AI ecosystem--covering data collection, simulation, model training, and deployment. The goal is to transfer skilled workers’ expertise to humanoids and tackle labor shortages. the first real step toward physical AGI is simply stepping into the real world.

CyberRobo

72,338 次观看 • 8 天前

🇨🇳 It has started. A new home service in China pairs human cleaners with autonomous AI robots to tackle household chores. Residents in Shenzhen can now book a service where a human professional and an autonomous robot arrive together to clean their home. Real houses present a chaotic mess of dropped toys and random furniture that confuse traditional machines. X Square Robot and a major service platform named 58[.]com decided to tackle this chaos by launching China's first robot cleaner service in March-26. Customers use an application to hire a cleaning crew that consists of 1 human worker and 1 robot. The human takes care of the tricky chores that require complex judgment. The robot handles the repetitive physical work like picking up trash and wiping down flat surfaces. This machine runs on a system called WALL-A, which acts as a single continuous AI brain rather than a list of pre-written rules. They built this AI foundation model to perceive its surroundings and make its own decisions without human guidance. It processes visual data and plans multi-step actions. And deploying these robots into actual homes now provides the massive amounts of extremely important training data to improve it continuously. Alibaba and ByteDance backed this project. IMO, if the foundational model behind it figures out how to navigate a messy living room without getting stuck, it can learn to operate in almost any other physical environment.

🇨🇳 It has started. A new home service in China pairs human cleaners with autonomous AI robots to tackle household chores. Residents in Shenzhen can now book a service where a human professional and an autonomous robot arrive together to clean their home. Real houses present a chaotic mess of dropped toys and random furniture that confuse traditional machines. X Square Robot and a major service platform named 58[.]com decided to tackle this chaos by launching China's first robot cleaner service in March-26. Customers use an application to hire a cleaning crew that consists of 1 human worker and 1 robot. The human takes care of the tricky chores that require complex judgment. The robot handles the repetitive physical work like picking up trash and wiping down flat surfaces. This machine runs on a system called WALL-A, which acts as a single continuous AI brain rather than a list of pre-written rules. They built this AI foundation model to perceive its surroundings and make its own decisions without human guidance. It processes visual data and plans multi-step actions. And deploying these robots into actual homes now provides the massive amounts of extremely important training data to improve it continuously. Alibaba and ByteDance backed this project. IMO, if the foundational model behind it figures out how to navigate a messy living room without getting stuck, it can learn to operate in almost any other physical environment.

Rohan Paul

52,996 次观看 • 3 个月前

Japan Just Built a HouseBot You Control Without Speaking and It Changes Everything! Donut Robotics has officially unveiled its first bipedal humanoid, Cinnamon 1, and instead of focusing on louder voices or bigger motors, the company went in the opposite direction. Silence. Cinnamon 1 introduces what Donut Robotics calls Silent Gesture Control, a system that allows the humanoid to be guided using simple hand and finger movements rather than spoken commands. This approach feels especially well suited for real world environments where traditional voice control falls apart. Busy factory floors. Construction sites filled with constant noise. Even quiet indoor settings where voice commands feel awkward or intrusive. It also opens the door for far more accessible human robot interaction, particularly for users with impairments. While the current Cinnamon 1 hardware is built on an OEM platform, the intelligence driving it is where Donut Robotics is placing its long term bet. The team is actively developing custom Vision Language Action AI that allows the robot to interpret what it sees, understand intent, and respond with physical action. The goal is not just smarter robots, but robots that feel more natural. Even more ambitious is the company’s plan for full domestic production. Donut Robotics has stated its intention to localize both manufacturing and AI development in Japan, reinforcing the country’s reputation for precision engineering and thoughtful robotics design. If timelines hold, Cinnamon 1 units are expected to begin deployment in factories and construction environments by the end of 2026. That puts this humanoid squarely in the category of near term reality rather than distant concept. The takeaway is simple but important. As humanoid robots move out of labs and into daily work environments, the winners may not be the loudest or flashiest machines. They may be the ones that understand us without a word being spoken.

Japan Just Built a HouseBot You Control Without Speaking and It Changes Everything! Donut Robotics has officially unveiled its first bipedal humanoid, Cinnamon 1, and instead of focusing on louder voices or bigger motors, the company went in the opposite direction. Silence. Cinnamon 1 introduces what Donut Robotics calls Silent Gesture Control, a system that allows the humanoid to be guided using simple hand and finger movements rather than spoken commands. This approach feels especially well suited for real world environments where traditional voice control falls apart. Busy factory floors. Construction sites filled with constant noise. Even quiet indoor settings where voice commands feel awkward or intrusive. It also opens the door for far more accessible human robot interaction, particularly for users with impairments. While the current Cinnamon 1 hardware is built on an OEM platform, the intelligence driving it is where Donut Robotics is placing its long term bet. The team is actively developing custom Vision Language Action AI that allows the robot to interpret what it sees, understand intent, and respond with physical action. The goal is not just smarter robots, but robots that feel more natural. Even more ambitious is the company’s plan for full domestic production. Donut Robotics has stated its intention to localize both manufacturing and AI development in Japan, reinforcing the country’s reputation for precision engineering and thoughtful robotics design. If timelines hold, Cinnamon 1 units are expected to begin deployment in factories and construction environments by the end of 2026. That puts this humanoid squarely in the category of near term reality rather than distant concept. The takeaway is simple but important. As humanoid robots move out of labs and into daily work environments, the winners may not be the loudest or flashiest machines. They may be the ones that understand us without a word being spoken.

The AI Robot Guy on X

257,928 次观看 • 5 个月前

LIGHTYEAR ROBOTICS has officially launched the M1, their first wheeled-legged robot. The M1 utilizes a self-developed parallel joint drive module, achieving a 40% efficiency increase over traditional structures while keeping the body lightweight. The design balances two core performance features: off-road mobility and high-speed cruising, enabling it to navigate complex environments like grass, gravel, and steep slopes using its all-scenario intelligent perception and autonomous navigation system. The robot is primarily aimed at enterprise applications, providing unmanned and highly reliable solutions for tasks ranging from outdoor inspection and emergency detection to material transport in challenging environments. #AI #HumanoidRobot #EmbodiedAI

LIGHTYEAR ROBOTICS has officially launched the M1, their first wheeled-legged robot. The M1 utilizes a self-developed parallel joint drive module, achieving a 40% efficiency increase over traditional structures while keeping the body lightweight. The design balances two core performance features: off-road mobility and high-speed cruising, enabling it to navigate complex environments like grass, gravel, and steep slopes using its all-scenario intelligent perception and autonomous navigation system. The robot is primarily aimed at enterprise applications, providing unmanned and highly reliable solutions for tasks ranging from outdoor inspection and emergency detection to material transport in challenging environments. #AI #HumanoidRobot #EmbodiedAI

RoboHub🤖

25,689 次观看 • 9 个月前

A new member joins the European humanoid robot team. German company Agile Robotics has launched its first industrial humanoid robot, Agile One, featuring intuitive human-robot interaction, dexterous hands (for grasping small screws and touching the screen), and AI-driven operation trained in the real world. It performs tasks such as material collection, handling, picking and placing, loading and unloading machine tools, tool handling, and precision operations. The emergence of humanoid robots by industrial robot companies is a natural progression; they are designed for areas inaccessible to traditional robotic arms and industrial robots, extending operational systems and enhancing business collaboration capabilities. Agile One will be manufactured in Germany in early 2026 and deployed for on-site training at customers to enhance its AI model capabilities. Soon, Europe will enter its second wave of automation driven by intelligent technology.

A new member joins the European humanoid robot team. German company Agile Robotics has launched its first industrial humanoid robot, Agile One, featuring intuitive human-robot interaction, dexterous hands (for grasping small screws and touching the screen), and AI-driven operation trained in the real world. It performs tasks such as material collection, handling, picking and placing, loading and unloading machine tools, tool handling, and precision operations. The emergence of humanoid robots by industrial robot companies is a natural progression; they are designed for areas inaccessible to traditional robotic arms and industrial robots, extending operational systems and enhancing business collaboration capabilities. Agile One will be manufactured in Germany in early 2026 and deployed for on-site training at customers to enhance its AI model capabilities. Soon, Europe will enter its second wave of automation driven by intelligent technology.

CyberRobo

72,567 次观看 • 8 个月前

Figure announces Project Go-Big: Internet-Scale Humanoid Pretraining and Direct Human-to-Robot Transfer The company has achieved a new milestone for the Helix AI model: after training exclusively on egocentric human data, Figure robots can now navigate real-world spaces from natural language commands such as “Go water the plants.” This also enabled a unified Helix model that can output both manipulation and navigation commands to the continuous control system. This ties into the Brookfield partnership that they announced yesterday. Brookfield is a global asset manager overseeing $1 trillion in assets, including 100,000 residential units, 500M square feet of commercial office space, and 160M square feet of logistics space. Using human video capture in a variety of Brookfield environments, Figure will amass critical pretraining data at scale, allowing them to explore the full potential of internet-scale embodied datasets for locomotion and manipulation.

Figure announces Project Go-Big: Internet-Scale Humanoid Pretraining and Direct Human-to-Robot Transfer The company has achieved a new milestone for the Helix AI model: after training exclusively on egocentric human data, Figure robots can now navigate real-world spaces from natural language commands such as “Go water the plants.” This also enabled a unified Helix model that can output both manipulation and navigation commands to the continuous control system. This ties into the Brookfield partnership that they announced yesterday. Brookfield is a global asset manager overseeing $1 trillion in assets, including 100,000 residential units, 500M square feet of commercial office space, and 160M square feet of logistics space. Using human video capture in a variety of Brookfield environments, Figure will amass critical pretraining data at scale, allowing them to explore the full potential of internet-scale embodied datasets for locomotion and manipulation.

The Humanoid Hub

32,879 次观看 • 10 个月前