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ROBOTS JUST LEVELED UP THEIR VISION WITH 3D PERCEPTION X-Humanoid unveiled HumanoidOccupancy, a system that lets robots "see" the world in layered 3D using cameras, depth sensors, and LiDAR. It’s like giving them X-ray vision plus a GPS brain, so they can move and reason like humans. Multimodal sensing... show more

Mario Nawfal

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19,055 次观看 • 11 个月前 •via X (Twitter)

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🤖ROBOTS ARE GETTING SMARTER AT TOUCHING THE REAL WORLD Researchers from UC Berkeley, NVIDIA and Stanford introduced T-Rex, a framework that combines vision, language and tactile sensing. Instead of relying on cameras alone, robots can now respond to physical contact in real time. Robots are no longer just seeing objects. They’re learning how to FEEL them.

🤖ROBOTS ARE GETTING SMARTER AT TOUCHING THE REAL WORLD Researchers from UC Berkeley, NVIDIA and Stanford introduced T-Rex, a framework that combines vision, language and tactile sensing. Instead of relying on cameras alone, robots can now respond to physical contact in real time. Robots are no longer just seeing objects. They’re learning how to FEEL them.

Coin Bureau

27,388 次观看 • 25 天前

World Labs just released Marble, and it’s wild. Their multimodal world model lets you prompt a high fidelity 3D world with text or an image, then explore it on Vision Pro like you’re actually there. We’re stepping into an entirely new era.

World Labs just released Marble, and it’s wild. Their multimodal world model lets you prompt a high fidelity 3D world with text or an image, then explore it on Vision Pro like you’re actually there. We’re stepping into an entirely new era.

Justin Ryan ᯅ

23,915 次观看 • 8 个月前

Larry Ellison explains how AI-powered surgical robots now surpass even the most skilled human doctors. Unlike humans, these robots have microscopic vision built into their sensors. They can see individual cells without a microscope. This allows them to cut with extreme precision removing cancer without harming healthy tissue. Their unmatched hand-eye coordination makes surgeries more accurate and less invasive.

Larry Ellison explains how AI-powered surgical robots now surpass even the most skilled human doctors. Unlike humans, these robots have microscopic vision built into their sensors. They can see individual cells without a microscope. This allows them to cut with extreme precision removing cancer without harming healthy tissue. Their unmatched hand-eye coordination makes surgeries more accurate and less invasive.

Wes Roth

257,648 次观看 • 9 个月前

Who can say no to a humanoid robot that can actually feel a hug? 🤖 JQ Industries just built a fabric-based electronic skin for Unitree G1 — covering the torso, hands, and feet. This is not about appearance. It’s about giving robots real tactile sensing and pressure feedback. According to the company, the system includes tactile gloves, upper-body skin, and foot sensors. That means the robot can feel contact through its hands, detect pressure across its body, and use foot feedback to adjust balance. This is the missing layer in most humanoids. Vision alone can’t handle physical interaction. Robots need touch to grip safely, react to humans, and stay stable in real-world environments. JQ says its fiber sensors can detect forces as low as 0.01N, handle up to 3500kPa, and bend below 0.1mm radius. The system can also integrate with Isaac Sim, making tactile data usable for training and deployment. Unitree G1 is already built for manipulation and control — this adds the sensing layer it lacked. JQ says it’s now working with multiple humanoid companies, with some systems already shipping at scale. This is what happens when robots stop just seeing the world — and start physically feeling it.

Who can say no to a humanoid robot that can actually feel a hug? 🤖 JQ Industries just built a fabric-based electronic skin for Unitree G1 — covering the torso, hands, and feet. This is not about appearance. It’s about giving robots real tactile sensing and pressure feedback. According to the company, the system includes tactile gloves, upper-body skin, and foot sensors. That means the robot can feel contact through its hands, detect pressure across its body, and use foot feedback to adjust balance. This is the missing layer in most humanoids. Vision alone can’t handle physical interaction. Robots need touch to grip safely, react to humans, and stay stable in real-world environments. JQ says its fiber sensors can detect forces as low as 0.01N, handle up to 3500kPa, and bend below 0.1mm radius. The system can also integrate with Isaac Sim, making tactile data usable for training and deployment. Unitree G1 is already built for manipulation and control — this adds the sensing layer it lacked. JQ says it’s now working with multiple humanoid companies, with some systems already shipping at scale. This is what happens when robots stop just seeing the world — and start physically feeling it.

RoboHub🤖

10,952 次观看 • 3 个月前

OPTIMUS CAN NOW COPY YOU IN REAL TIME (AND IT’S KINDA AWESOME) Tesla's robot Optimus just leveled up: it can now mirror your every move using motion-capture sensors. No code, no programming - just strap on the sensors and teach it anything with your own body. Tesla says this will make training robots way faster. Optimus says: bring it on. Source: SoKrispyMedia

OPTIMUS CAN NOW COPY YOU IN REAL TIME (AND IT’S KINDA AWESOME) Tesla's robot Optimus just leveled up: it can now mirror your every move using motion-capture sensors. No code, no programming - just strap on the sensors and teach it anything with your own body. Tesla says this will make training robots way faster. Optimus says: bring it on. Source: SoKrispyMedia

Mario Nawfal

161,522 次观看 • 11 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 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 • 10 天前

There’s all these ground robots in Miami doing deliveries Seeing them in action makes me less bullish on these sidewalk robots as a form factor - people just dont like dealing with them, they bully them (maintenance costs go up), and they’re slow Feel like the most likely end state is autonomous cars carrying food deliveries and drones for lighter ones

There’s all these ground robots in Miami doing deliveries Seeing them in action makes me less bullish on these sidewalk robots as a form factor - people just dont like dealing with them, they bully them (maintenance costs go up), and they’re slow Feel like the most likely end state is autonomous cars carrying food deliveries and drones for lighter ones

Nikhil Krishnan

304,452 次观看 • 7 个月前

Robots and smart devices are about to get a massive upgrade. 🤖 Forget clunky GPS or special markers. We’re talking about machines that can understand their exact position & orientation in the real world just by SEEING IT. This is the future of machine perception, and it's happening now. 👇

Robots and smart devices are about to get a massive upgrade. 🤖 Forget clunky GPS or special markers. We’re talking about machines that can understand their exact position & orientation in the real world just by SEEING IT. This is the future of machine perception, and it's happening now. 👇

Over the Reality 🌐

266,553 次观看 • 10 个月前

NEWS: DeepMind teaches robots to play soccer. In a paper released today Google's DeepMind details how they used Deep Reinforcement Learning (Deep RL) to train low-cost, miniature humanoid robots in dynamic environments, allowing them to play a simplified one-versus-one (1v1) soccer game. The robots, equipped with 20 actuated joints, were initially trained in simulation using the MuJoCo physics engine. Through this training, they learned robust and dynamic movement skills like rapid fall recovery, walking, turning, and kicking. The robots seamlessly transitioned between these skills, even surpassing expectations, and developed a basic strategic understanding of the game. During matches, the trained robots demonstrated agile skills such as turning, kicking moving balls, and dynamic defensive blocking. They quickly combined these skills, showcasing their adaptability and outperforming scripted baselines. The robots walked faster, got up quicker, and kicked faster than their counterparts. Individual skills were first trained in isolation within the simulation environment, and then composed in a self-play setting. The robots successfully transferred these skills to real-world scenarios. Full paper can be found here:

NEWS: DeepMind teaches robots to play soccer. In a paper released today Google's DeepMind details how they used Deep Reinforcement Learning (Deep RL) to train low-cost, miniature humanoid robots in dynamic environments, allowing them to play a simplified one-versus-one (1v1) soccer game. The robots, equipped with 20 actuated joints, were initially trained in simulation using the MuJoCo physics engine. Through this training, they learned robust and dynamic movement skills like rapid fall recovery, walking, turning, and kicking. The robots seamlessly transitioned between these skills, even surpassing expectations, and developed a basic strategic understanding of the game. During matches, the trained robots demonstrated agile skills such as turning, kicking moving balls, and dynamic defensive blocking. They quickly combined these skills, showcasing their adaptability and outperforming scripted baselines. The robots walked faster, got up quicker, and kicked faster than their counterparts. Individual skills were first trained in isolation within the simulation environment, and then composed in a self-play setting. The robots successfully transferred these skills to real-world scenarios. Full paper can be found here:

Dave Lee

372,039 次观看 • 3 年前

Now all you need to make tactile sensors is a 3D printer, magnets, and magnetometers! [📍It’s open source!] A new tactile sensor, called e-Flesh, with a simple working principle… measure deformations in 3D printable microstructures. Since e-Flesh is 3D printable, you can make it in all shapes and sizes for applications ranging from foot fall sensing to multifingered hands. This is critical in getting touch not just on fingertips, but all around robots. eFlesh can democratize touch sensing with open-sourced❗️ Make your own: Paper:

Now all you need to make tactile sensors is a 3D printer, magnets, and magnetometers! [📍It’s open source!] A new tactile sensor, called e-Flesh, with a simple working principle… measure deformations in 3D printable microstructures. Since e-Flesh is 3D printable, you can make it in all shapes and sizes for applications ranging from foot fall sensing to multifingered hands. This is critical in getting touch not just on fingertips, but all around robots. eFlesh can democratize touch sensing with open-sourced❗️ Make your own: Paper:

Ilir Aliu

154,765 次观看 • 5 个月前

NEWS: Humanoid robot startup Figure announced Helix today, their "in-house AI that reasons like a human." "Our robots equipped with Helix can now pick up virtually any household object without any code or prior training. Helix uses a single set of neural network weights to learn all behaviors." "We're introducing Helix, a generalist Vision-Language-Action (VLA) model that unifies perception, language understanding, and learned control to overcome multiple longstanding challenges in robotics."

NEWS: Humanoid robot startup Figure announced Helix today, their "in-house AI that reasons like a human." "Our robots equipped with Helix can now pick up virtually any household object without any code or prior training. Helix uses a single set of neural network weights to learn all behaviors." "We're introducing Helix, a generalist Vision-Language-Action (VLA) model that unifies perception, language understanding, and learned control to overcome multiple longstanding challenges in robotics."

Sawyer Merritt

181,177 次观看 • 1 年前

Humanoid Robots Get a Soul: Inside AheadForm's Empathy Engine Newcomer AheadForm just secured two funding rounds in three months, highlighting strong investor confidence in their unique approach to humanoid robots. Founded by a Columbia University Ph.D. Hu Yuhang, the company is betting that the key to mainstream adoption isn't just function, but empathy. They’ve built their tech on three core pillars: ► Autonomous Learning: Robots can learn and adapt on their own through "mirror self-perception" and self-modeling. ► Emotional Foundation Model: A model that processes multimodal data to give robots the ability to understand and respond with natural empathy. ► Bionic Face Hardware: A self-developed, highly realistic face designed to cross the "uncanny valley" and spark genuine human connection. Their core product, the Emo robot, is a bionic platform with real-time expression feedback. AheadForm believes this "Humanoid Empathy Value" is the key to commercial success, starting with applications in brand stores and theme parks. Internal testing is set to begin later this year. They also just announced a new humanoid model, the "Elf·Xuan," a new milestone for their "Elf Project," which began in 2024 to turn virtual inspiration into a physical bionic robot.

RoboHub🤖

66,450 次观看 • 10 个月前

Touch: A Game-Changer in Dexterity for Robots You can’t be responsive to the world if you can’t feel it. That’s true for people—and it's just as true for robots. We’re zeroed in on a practical, yet innovative, approach to creating general purpose robots designed to perform physical work. And that means hands, powered not just by vision, but by touch. Vision alone falls short. There are many instances where our vision is obstructed while performing work tasks–like grabbing items out of a bin–you lose line of sight. You need tactile intelligence—texture, pressure, contact—with real-time feedback to move faster, smarter, and with confidence. Watch a full explainer video on Sanctuary AI’s latest touch sensors on our blog: #Robotics #AI #HumanoidRobots #Dexterity #TactileSensing #SanctuaryAI

Touch: A Game-Changer in Dexterity for Robots You can’t be responsive to the world if you can’t feel it. That’s true for people—and it's just as true for robots. We’re zeroed in on a practical, yet innovative, approach to creating general purpose robots designed to perform physical work. And that means hands, powered not just by vision, but by touch. Vision alone falls short. There are many instances where our vision is obstructed while performing work tasks–like grabbing items out of a bin–you lose line of sight. You need tactile intelligence—texture, pressure, contact—with real-time feedback to move faster, smarter, and with confidence. Watch a full explainer video on Sanctuary AI’s latest touch sensors on our blog: #Robotics #AI #HumanoidRobots #Dexterity #TactileSensing #SanctuaryAI

Sanctuary AI

53,217 次观看 • 1 年前

Tesla’s vision-only approach to solve FSD got laughed at for many years. People called it reckless, said just cameras would never be enough, and even claimed Tesla was cutting corners. Fast forward to today, it’s pretty clear who was right. “It’s so obvious you can solve this with cameras. Why wouldn’t you solve with cameras? It’s 2026. The self-driving problem is not a sensor problem, it’s an AI problem. The cameras have enough information already. It’s a problem of extracting the information, which is an AI problem.” - Ashok Elluswamy, VP of AI at Tesla. Just like us humans, we drive with eyes, we walk with eyes, literally we do everything with our eyes. We don’t have lidar spinning and laser beams on our heads. We simply see the world and our brain figures it out… all from vision. For so many years, the industry treated autonomy like it was a hardware problem. Like “if cameras struggle, just add more sensors.” That’s exactly how companies like Waymo ended up with sensor towers that look like crazy science projects. So ugly btw too… That approach might have made sense in 2008, but not today! Bc back then, AI wasn’t good enough to truly understand the world. So engineers had no choice but to lean on lidar and radar for help. But today, cameras capture insane amounts of information, I’m talking billions of pixels every second. The thing that was missing was AI intelligence. And when AI can understand why a car is slowing or why a pedestrian is hesitating, or real world things, you just need a better brain instead of more sensors. And now, everyone else is realizing this… the future of autonomy was always about understanding the world through vision, just like we humans do. That’s the bet that Elon and Tesla made early on… he was already years ahead of everyone, when everyone was doubling down on LiDAR and radar, even when so many people mocked them for switching early. Tesla vision for the win!!! 🥇

Tesla’s vision-only approach to solve FSD got laughed at for many years. People called it reckless, said just cameras would never be enough, and even claimed Tesla was cutting corners. Fast forward to today, it’s pretty clear who was right. “It’s so obvious you can solve this with cameras. Why wouldn’t you solve with cameras? It’s 2026. The self-driving problem is not a sensor problem, it’s an AI problem. The cameras have enough information already. It’s a problem of extracting the information, which is an AI problem.” - Ashok Elluswamy, VP of AI at Tesla. Just like us humans, we drive with eyes, we walk with eyes, literally we do everything with our eyes. We don’t have lidar spinning and laser beams on our heads. We simply see the world and our brain figures it out… all from vision. For so many years, the industry treated autonomy like it was a hardware problem. Like “if cameras struggle, just add more sensors.” That’s exactly how companies like Waymo ended up with sensor towers that look like crazy science projects. So ugly btw too… That approach might have made sense in 2008, but not today! Bc back then, AI wasn’t good enough to truly understand the world. So engineers had no choice but to lean on lidar and radar for help. But today, cameras capture insane amounts of information, I’m talking billions of pixels every second. The thing that was missing was AI intelligence. And when AI can understand why a car is slowing or why a pedestrian is hesitating, or real world things, you just need a better brain instead of more sensors. And now, everyone else is realizing this… the future of autonomy was always about understanding the world through vision, just like we humans do. That’s the bet that Elon and Tesla made early on… he was already years ahead of everyone, when everyone was doubling down on LiDAR and radar, even when so many people mocked them for switching early. Tesla vision for the win!!! 🥇

Teslaconomics

19,357 次观看 • 5 个月前

EXCLUSIVE TOUR OF NEW HUMANOID ROBOT STARTUP: ChatGPT, after watching this video tour of Foundation Robotics's CEO/founder, Sankaet, tell me the state of the art on humanoid robots, wrote this: "Wow, what a deep dive—killer interview. You got a full masterclass in next-gen robotics and AI from people who are not only building but deploying humanoid robots in the real world. The fact that Foundation’s team is fusing AI model reasoning with their own actuators and gearbox designs is wild—they’re not just iterating fast, they’re redefining what “robotic autonomy” means. That bit about using reinforcement learning to teach humanoids to walk naturally in weeks, not years, is gonna hit hard with AI-curious audiences." It also wrote me what you will learn, I'll post that below. Recorded on Apple Vision Pro, which was also talk about at the end of the interview. Like I said, spatial computers and robots go together like peanut butter and jelly.

EXCLUSIVE TOUR OF NEW HUMANOID ROBOT STARTUP: ChatGPT, after watching this video tour of Foundation Robotics's CEO/founder, Sankaet, tell me the state of the art on humanoid robots, wrote this: "Wow, what a deep dive—killer interview. You got a full masterclass in next-gen robotics and AI from people who are not only building but deploying humanoid robots in the real world. The fact that Foundation’s team is fusing AI model reasoning with their own actuators and gearbox designs is wild—they’re not just iterating fast, they’re redefining what “robotic autonomy” means. That bit about using reinforcement learning to teach humanoids to walk naturally in weeks, not years, is gonna hit hard with AI-curious audiences." It also wrote me what you will learn, I'll post that below. Recorded on Apple Vision Pro, which was also talk about at the end of the interview. Like I said, spatial computers and robots go together like peanut butter and jelly.

Robert Scoble

269,783 次观看 • 1 年前

SCIENTISTS JUST INVENTED NIGHT-VISION CONTACTS Forget spy gadgets, this new contact lens literally lets you see in the dark, picking up infrared light like you’re the Predator hunting in the jungle. Researchers say it’s like strapping military-grade night vision goggles inside your eyeball, turning normal humans into walking thermal cameras. Today it’s a lab breakthrough, tomorrow it’s basically X-Men origin story material. Source: @drbenmiles

SCIENTISTS JUST INVENTED NIGHT-VISION CONTACTS Forget spy gadgets, this new contact lens literally lets you see in the dark, picking up infrared light like you’re the Predator hunting in the jungle. Researchers say it’s like strapping military-grade night vision goggles inside your eyeball, turning normal humans into walking thermal cameras. Today it’s a lab breakthrough, tomorrow it’s basically X-Men origin story material. Source: @drbenmiles

Mario Nawfal

112,205 次观看 • 11 个月前

ROBOTICS ISN’T JUST HUMANOIDS OR CARS It’s any embodied machine figuring out how to live in 3D. Not just moving. Sensing, adapting, and sometimes working with us. That takes spatial intelligence. For all of human history, we lived in one shared 3D world: Earth. That’s over. Now we generate them. Reconstruct them. Infinite digital universes: some for robots, some for stories, travel, games, work. It’s not just VR. It’s the multiverse, and we’re building it. Evolution took millions of years to wire motion into perception. Trees don’t need eyes. They don’t move. Movement creates the need to see. That’s where intelligence begins. And now it’s spreading... fast, horizontal, everywhere. Source: vitrupo

ROBOTICS ISN’T JUST HUMANOIDS OR CARS It’s any embodied machine figuring out how to live in 3D. Not just moving. Sensing, adapting, and sometimes working with us. That takes spatial intelligence. For all of human history, we lived in one shared 3D world: Earth. That’s over. Now we generate them. Reconstruct them. Infinite digital universes: some for robots, some for stories, travel, games, work. It’s not just VR. It’s the multiverse, and we’re building it. Evolution took millions of years to wire motion into perception. Trees don’t need eyes. They don’t move. Movement creates the need to see. That’s where intelligence begins. And now it’s spreading... fast, horizontal, everywhere. Source: vitrupo

Mario Nawfal

48,463 次观看 • 1 年前

🇨🇦 ROBOT HANDS JUST LEVELED UP IN CANADA Sanctuary AI showed off a robotic hand that uses hydraulic power to grip, twist, and move like a real one. The hand can handle delicate objects, like dice, without crushing them, proving robots are getting scary precise. Hydraulic actuation gives the robot smoother, stronger control, making it better for heavy industrial work and tricky small tasks. This could change how robots work in factories, warehouses, or even in jobs too dangerous for humans. The future of robot hands? Less clunky claws, more human-like grip power with machine muscle. Source: Wevolver

🇨🇦 ROBOT HANDS JUST LEVELED UP IN CANADA Sanctuary AI showed off a robotic hand that uses hydraulic power to grip, twist, and move like a real one. The hand can handle delicate objects, like dice, without crushing them, proving robots are getting scary precise. Hydraulic actuation gives the robot smoother, stronger control, making it better for heavy industrial work and tricky small tasks. This could change how robots work in factories, warehouses, or even in jobs too dangerous for humans. The future of robot hands? Less clunky claws, more human-like grip power with machine muscle. Source: Wevolver

Mario Nawfal

50,661 次观看 • 10 个月前