Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Most humanoid projects talk about real work. Very few last an hour on a real line. This week I saw a case that matters for anyone building robots, perception, or physical AI. Kinisi deployed its first mobile manipulation system into a live recycling facility. Not a demo. Not a...

IlirAliu_'s profile picture

Ilir Aliu

55,507 subscribers

24,815 Aufrufe • vor 10 Monaten •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

TESLA HALTED MODEL S AND MODEL X PRODUCTION TO BUILD AN ARMY OF OPTIMUS ROBOTS The Fremont assembly line was torn down in 46 days. In its place, Tesla is building a line for humanoid production, aiming for a million units a year A humanoid robot is a body shaped like a human. Physical AI is the intelligence that controls that body Walking and making coffee is often just imitation learning from a scripted routine. But once the environment shifts, the learned trick stops working Language models had the entire internet to train on. Robotics has nothing close to that scale of data, which is why one giant brain hasn't worked for anyone yet The industry is moving toward modularity instead - separate models for vision, movement, and planning, each improved on its own The real question is no longer whether a robot can move impressively. It's whether it can pull its sensors into one picture of the world and adapt to whatever wasn't scripted for it
0:12

TESLA HALTED MODEL S AND MODEL X PRODUCTION TO BUILD AN ARMY OF OPTIMUS ROBOTS The Fremont assembly line was torn down in 46 days. In its place, Tesla is building a line for humanoid production, aiming for a million units a year A humanoid robot is a body shaped like a human. Physical AI is the intelligence that controls that body Walking and making coffee is often just imitation learning from a scripted routine. But once the environment shifts, the learned trick stops working Language models had the entire internet to train on. Robotics has nothing close to that scale of data, which is why one giant brain hasn't worked for anyone yet The industry is moving toward modularity instead - separate models for vision, movement, and planning, each improved on its own The real question is no longer whether a robot can move impressively. It's whether it can pull its sensors into one picture of the world and adapt to whatever wasn't scripted for it

iamigorekk

22,205 Aufrufe • vor 1 Monat

Excited to share a few presentations, demos, and workshop talks from our group and collaborators at #ICRA2026! We will present recent work on real-to-sim-to-real robot policy evaluation, model-based planning with learned dynamics, and multi-modal manipulation. We will also have a joint live demo between SceniX and Analog Devices, Inc. on real-to-sim-to-real cable manipulation at the ICRA exhibition. This is a small teaser of what we have been building, with more to come soon! If you are at ICRA, please stop by the sessions or the demo booth. Happy to chat about robot learning, simulation, world models, and sim-to-real!
0:15

Excited to share a few presentations, demos, and workshop talks from our group and collaborators at #ICRA2026! We will present recent work on real-to-sim-to-real robot policy evaluation, model-based planning with learned dynamics, and multi-modal manipulation. We will also have a joint live demo between SceniX and Analog Devices, Inc. on real-to-sim-to-real cable manipulation at the ICRA exhibition. This is a small teaser of what we have been building, with more to come soon! If you are at ICRA, please stop by the sessions or the demo booth. Happy to chat about robot learning, simulation, world models, and sim-to-real!

Yunzhu Li

11,344 Aufrufe • vor 4 Monaten

Furniture assembly is the task everyone name-drops and nobody actually attempts at real scale. Every demo I have seen is a scaled down IKEA leg or a single arm on a toy chair. This paper does it properly, real scale, bimanual, up to 7 subtasks and 1,550 control steps per episode, and it is validated on a real Kinova Gen3, not just in sim. That real-robot number is the one that matters: only a 16 percent drop on the hardest task going from simulation to hardware. That is a small enough gap to take seriously, and it did not happen by accident. They built a VR teleoperation rig specifically for coordinated dual-arm collection, because generic single-arm teleop setups do not capture the coordination real assembly needs, and the model predicts a continuous progress signal alongside the action chunk rather than a discrete subtask label, letting it auto-transition and catch drift before it compounds into total failure. The simulation ablation is what got them there, 48 to 80 percent over baselines, with another 21 points from their perception and control design study alone, but that is groundwork, not the headline. Watch the video, there is a clip of the robot misgrasping the seat panel, reopening the gripper, and regrasping on its own. That is not scripted recovery behaviour, it emerged from training, and it emerged on hardware. Excellent work from the team from Mitsubishi Electric Research Laboratories, with Oxford and UNC Chapel Hill Clinical Laboratory Science. Video and project page in comments. #Robotics #Manipulation #VLA
0:14

Furniture assembly is the task everyone name-drops and nobody actually attempts at real scale. Every demo I have seen is a scaled down IKEA leg or a single arm on a toy chair. This paper does it properly, real scale, bimanual, up to 7 subtasks and 1,550 control steps per episode, and it is validated on a real Kinova Gen3, not just in sim. That real-robot number is the one that matters: only a 16 percent drop on the hardest task going from simulation to hardware. That is a small enough gap to take seriously, and it did not happen by accident. They built a VR teleoperation rig specifically for coordinated dual-arm collection, because generic single-arm teleop setups do not capture the coordination real assembly needs, and the model predicts a continuous progress signal alongside the action chunk rather than a discrete subtask label, letting it auto-transition and catch drift before it compounds into total failure. The simulation ablation is what got them there, 48 to 80 percent over baselines, with another 21 points from their perception and control design study alone, but that is groundwork, not the headline. Watch the video, there is a clip of the robot misgrasping the seat panel, reopening the gripper, and regrasping on its own. That is not scripted recovery behaviour, it emerged from training, and it emerged on hardware. Excellent work from the team from Mitsubishi Electric Research Laboratories, with Oxford and UNC Chapel Hill Clinical Laboratory Science. Video and project page in comments. #Robotics #Manipulation #VLA

Stephen James

14,952 Aufrufe • vor 2 Monaten

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 →
0:14

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 Aufrufe • vor 1 Monat

AI in robotics gets all the attention right now, but sometimes the most interesting work is very practical. Viet built a small vision system that counts potatoes on a conveyor belt. No giant dataset. No huge model. Just a clear problem and a smart setup. He used Ultralytics’ ObjectCounter, trained a tiny YOLO11 nano model, and because there was no potato dataset, he annotated a single frame with SAM 2 and trained from that. One frame. Still works across the whole video. It is a good reminder that useful AI in industry often looks like this. Focused. Lightweight. Solves a real task. If you work in manufacturing or robotics, these small systems are usually the fastest wins. They save time, reduce errors, and do not need massive infrastructure. Nice work, Viet. His projects: —- Weekly robotics and AI insights. Subscribe free:
0:13

AI in robotics gets all the attention right now, but sometimes the most interesting work is very practical. Viet built a small vision system that counts potatoes on a conveyor belt. No giant dataset. No huge model. Just a clear problem and a smart setup. He used Ultralytics’ ObjectCounter, trained a tiny YOLO11 nano model, and because there was no potato dataset, he annotated a single frame with SAM 2 and trained from that. One frame. Still works across the whole video. It is a good reminder that useful AI in industry often looks like this. Focused. Lightweight. Solves a real task. If you work in manufacturing or robotics, these small systems are usually the fastest wins. They save time, reduce errors, and do not need massive infrastructure. Nice work, Viet. His projects: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

1,676,995 Aufrufe • vor 10 Monaten

A simple MPC controller for path tracking implemented in python. Few resources actually teach it without assuming a PhD first. This open-source project does exactly that: An iterative MPC tracking controller (built with CVXPY and MuJoCo) that shows how to make a robot (or autonomous vehicle) follow a path and avoid obstacles in real time. → Autonomous logistics, robot navigation, vehicle control; there's a path-following algorithm behind all of it. → Most implementations are black boxes. This one shows you what's actually happening. → It uses convex optimization (no non-linear solver), and still handles real vehicle physics through iterative linearization. What makes it practical is that you can run it with the mushr car model in MuJoCo, or headless without any physics simulation if you just want to understand the logic. If you're making the jump from classical control to real-time optimisation, this is a very good starting point. 📌 Repository: ——- Weekly robotics and AI insights. Subscribe free:
0:14

A simple MPC controller for path tracking implemented in python. Few resources actually teach it without assuming a PhD first. This open-source project does exactly that: An iterative MPC tracking controller (built with CVXPY and MuJoCo) that shows how to make a robot (or autonomous vehicle) follow a path and avoid obstacles in real time. → Autonomous logistics, robot navigation, vehicle control; there's a path-following algorithm behind all of it. → Most implementations are black boxes. This one shows you what's actually happening. → It uses convex optimization (no non-linear solver), and still handles real vehicle physics through iterative linearization. What makes it practical is that you can run it with the mushr car model in MuJoCo, or headless without any physics simulation if you just want to understand the logic. If you're making the jump from classical control to real-time optimisation, this is a very good starting point. 📌 Repository: ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

16,064 Aufrufe • vor 19 Tagen

"Europe has already lost the AI race." I hear this bashing almost every single week on LinkedIn and X. Not so much when I talk to the teams who are actually working on it. They are trying to do something about it. Take Feyer. They are not building another generic wrapper application. Feyer is developing AI systems that autonomously design novel industrial hardware. Their neural explorers are coupled directly with differentiable physics simulations, allowing them to search enormous design spaces and discover hardware that humans might never come up with themselves. That could accelerate innovation across everything from lasers and quantum technology to microchip production. On September 9, Cyber Valley celebrates its 10th anniversary here in Tübingen. And I want to show and talk about companies like Feyer that are sitting here. They are a pretty good example of what can happen when world-class research turns into an ambitious company. They are building right here in Tübingen and just secured €3 million in the SPRIND, Federal Agency for Breakthrough Innovation - Bundesagentur für Sprunginnovationen Next Frontier AI Challenge. I spent some time on a call with their CTO Sören Arlt last week, and the level of technical ambition there is exactly what this ecosystem needs right now. And Feyer is part of a much bigger bet. SPRIND, Federal Agency for Breakthrough Innovation is deploying €125 million over 24 months to build three internationally competitive European frontier AI labs. Ten teams start with up to €3 million each, six can advance with another €8 million, and the final three can receive another €15.5 million each. Up to €26.5 million per winning team. And this is not a research thesis invented for a startup pitch. The work builds on years of research by Sören Arlt, Mario Krenn and their collaborators into machine-driven scientific discovery. I am going to share a lot more about what Sören Arlt, Jonathan Klimesch, Mario Krenn and the rest of the Feyer team are building very soon. If you want to see what European frontier AI can actually look like, keep an eye on them. Follow for more insights into AI and robotics. {Quick animation by me for now. We’ll have much better visuals to share over the next few weeks ;)}
0:10

"Europe has already lost the AI race." I hear this bashing almost every single week on LinkedIn and X. Not so much when I talk to the teams who are actually working on it. They are trying to do something about it. Take Feyer. They are not building another generic wrapper application. Feyer is developing AI systems that autonomously design novel industrial hardware. Their neural explorers are coupled directly with differentiable physics simulations, allowing them to search enormous design spaces and discover hardware that humans might never come up with themselves. That could accelerate innovation across everything from lasers and quantum technology to microchip production. On September 9, Cyber Valley celebrates its 10th anniversary here in Tübingen. And I want to show and talk about companies like Feyer that are sitting here. They are a pretty good example of what can happen when world-class research turns into an ambitious company. They are building right here in Tübingen and just secured €3 million in the SPRIND, Federal Agency for Breakthrough Innovation - Bundesagentur für Sprunginnovationen Next Frontier AI Challenge. I spent some time on a call with their CTO Sören Arlt last week, and the level of technical ambition there is exactly what this ecosystem needs right now. And Feyer is part of a much bigger bet. SPRIND, Federal Agency for Breakthrough Innovation is deploying €125 million over 24 months to build three internationally competitive European frontier AI labs. Ten teams start with up to €3 million each, six can advance with another €8 million, and the final three can receive another €15.5 million each. Up to €26.5 million per winning team. And this is not a research thesis invented for a startup pitch. The work builds on years of research by Sören Arlt, Mario Krenn and their collaborators into machine-driven scientific discovery. I am going to share a lot more about what Sören Arlt, Jonathan Klimesch, Mario Krenn and the rest of the Feyer team are building very soon. If you want to see what European frontier AI can actually look like, keep an eye on them. Follow for more insights into AI and robotics. {Quick animation by me for now. We’ll have much better visuals to share over the next few weeks ;)}

Ilir Aliu

10,745 Aufrufe • vor 1 Monat

Interestingly, Xynova’s technological approach shares the same origins as the dexterous hand technology used in Optimus v3 (though Elon has noted that this design still needs further refinement). The Flex2 is an upgraded version built on the Flex1: v1 featured 25 DOF and used a cable-driven system; the v2 introduces direct drive, which reduces the DOF to 23 but also sheds 400g in weight. It seems a hybrid drive mechanism may be the more practical solution. In March this year, following the successful completion of its Series A funding round (with investors including Xiaomi and others), this robotics company--founded in 2024--began construction of a large-scale production facility. Spanning over 5,000 square meters, the base is designed to achieve an annual output of 200,000 miniature electric cylinders and 10,000 dexterous hands. However, hardware alone is far from enough. A truly capable dexterous hand must be the result of the co-evolution of data, models, and the physical hardware. In other words, in addition to mass production, Xynova is simultaneously developing a complete integrated system that combines perception capabilities, robotic manipulation intelligence, and hand-specific coordination. This is essentially a foundational robotic module. Yet its applications go far beyond that. It can be directly adapted to industrial robotic arms on production lines, as well as integrated into the bodies of humanoid robots. That said, what I’m most eager to see is its use in advanced bionic prosthetics for humans. If it can successfully demonstrate this expanded capability, its impact will reach well beyond the realm of humanoid robots. (Cyborg)
0:16

Interestingly, Xynova’s technological approach shares the same origins as the dexterous hand technology used in Optimus v3 (though Elon has noted that this design still needs further refinement). The Flex2 is an upgraded version built on the Flex1: v1 featured 25 DOF and used a cable-driven system; the v2 introduces direct drive, which reduces the DOF to 23 but also sheds 400g in weight. It seems a hybrid drive mechanism may be the more practical solution. In March this year, following the successful completion of its Series A funding round (with investors including Xiaomi and others), this robotics company--founded in 2024--began construction of a large-scale production facility. Spanning over 5,000 square meters, the base is designed to achieve an annual output of 200,000 miniature electric cylinders and 10,000 dexterous hands. However, hardware alone is far from enough. A truly capable dexterous hand must be the result of the co-evolution of data, models, and the physical hardware. In other words, in addition to mass production, Xynova is simultaneously developing a complete integrated system that combines perception capabilities, robotic manipulation intelligence, and hand-specific coordination. This is essentially a foundational robotic module. Yet its applications go far beyond that. It can be directly adapted to industrial robotic arms on production lines, as well as integrated into the bodies of humanoid robots. That said, what I’m most eager to see is its use in advanced bionic prosthetics for humans. If it can successfully demonstrate this expanded capability, its impact will reach well beyond the realm of humanoid robots. (Cyborg)

CyberRobo

45,071 Aufrufe • vor 4 Monaten

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
0:12

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 Aufrufe • vor 2 Monaten

We’ve been working on something for 8 months. If you write emails, you know the struggle. You’re digging through your inbox, scrolling screenshots, trying to find inspiration that shouldn’t take this long. So Jimmy Kim and I built a solution. One place to search around 1,000,000 real ecommerce emails. But here’s the part I’m most excited about: You can click any email and get instant AI analysis on what worked, what didn’t, and why it converted. Real examples. Real breakdowns. Real insights you can use today. Free trial is live if you want to try it:
0:14

We’ve been working on something for 8 months. If you write emails, you know the struggle. You’re digging through your inbox, scrolling screenshots, trying to find inspiration that shouldn’t take this long. So Jimmy Kim and I built a solution. One place to search around 1,000,000 real ecommerce emails. But here’s the part I’m most excited about: You can click any email and get instant AI analysis on what worked, what didn’t, and why it converted. Real examples. Real breakdowns. Real insights you can use today. Free trial is live if you want to try it:

Chase Dimond | Email Marketing Nerd 📧

38,831 Aufrufe • vor 10 Monaten

Trained on zero real-world data. Learned to walk, pick up boxes, and follow multi-step instructions... in the REAL world. ( 📌 Paper below) Researchers from Amazon FAR, Berkeley, Stanford, and CMU scanned real rooms with an iPhone, rebuilt them as 3D Gaussian Splatting scenes, then generated 48,000 synthetic trajectories of a Unitree G1 walking, grasping, and placing objects inside those virtual replicas. They rendered the robot's first-person camera view from each run and paired it with the matching language instruction and motion data. That's the dataset every humanoid team needs and nobody has: synced egocentric video + language + kinematics, at scale. Instead of collecting it in the real world, they manufactured it. They trained a vision-language-kinematics policy on that synthetic data alone, then deployed it on the physical G1 across five task types: navigation to a named object, lifting boxes of three different sizes with no per-size tuning, chained multi-step tasks, robustness to mid-task layout changes and flickering lights, and multi-minute long-horizon runs. No real-world fine-tuning at any point. Real-world interaction data has been the hard limit on humanoid learning... slow, expensive, and small. If scanning a room once and synthesizing thousands of labeled interactions holds up as a general recipe, that limit moves. Data stops being the bottleneck robotics teams have to solve for. 📌 Paper: Project: ——- Weekly robotics and AI insights. Subscribe free:
0:15

Trained on zero real-world data. Learned to walk, pick up boxes, and follow multi-step instructions... in the REAL world. ( 📌 Paper below) Researchers from Amazon FAR, Berkeley, Stanford, and CMU scanned real rooms with an iPhone, rebuilt them as 3D Gaussian Splatting scenes, then generated 48,000 synthetic trajectories of a Unitree G1 walking, grasping, and placing objects inside those virtual replicas. They rendered the robot's first-person camera view from each run and paired it with the matching language instruction and motion data. That's the dataset every humanoid team needs and nobody has: synced egocentric video + language + kinematics, at scale. Instead of collecting it in the real world, they manufactured it. They trained a vision-language-kinematics policy on that synthetic data alone, then deployed it on the physical G1 across five task types: navigation to a named object, lifting boxes of three different sizes with no per-size tuning, chained multi-step tasks, robustness to mid-task layout changes and flickering lights, and multi-minute long-horizon runs. No real-world fine-tuning at any point. Real-world interaction data has been the hard limit on humanoid learning... slow, expensive, and small. If scanning a room once and synthesizing thousands of labeled interactions holds up as a general recipe, that limit moves. Data stops being the bottleneck robotics teams have to solve for. 📌 Paper: Project: ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

12,950 Aufrufe • vor 2 Monaten

HUMANOID ROBOTS ARE STARTING TO WALK AMONG US At first, this looks like a completely normal street video. A woman is walking down the sidewalk on a sunny day, with people, stores and traffic around her. Then you notice who’s walking beside her. A humanoid robot. There’s no robotics conference, no laboratory and no team of engineers around it. The robot is simply walking through the city next to her as if this is already part of everyday life. Until recently, humanoid robots were mostly something we saw in controlled demonstrations, factories and tech presentations. Now they’re beginning to appear in ordinary public spaces, walking alongside people without looking completely out of place. The biggest shift isn’t just how advanced these machines are becoming. It’s how quickly they’re starting to blend into the real world. At this pace, seeing a humanoid robot walking next to you in public might soon stop feeling unusual at all
0:10

HUMANOID ROBOTS ARE STARTING TO WALK AMONG US At first, this looks like a completely normal street video. A woman is walking down the sidewalk on a sunny day, with people, stores and traffic around her. Then you notice who’s walking beside her. A humanoid robot. There’s no robotics conference, no laboratory and no team of engineers around it. The robot is simply walking through the city next to her as if this is already part of everyday life. Until recently, humanoid robots were mostly something we saw in controlled demonstrations, factories and tech presentations. Now they’re beginning to appear in ordinary public spaces, walking alongside people without looking completely out of place. The biggest shift isn’t just how advanced these machines are becoming. It’s how quickly they’re starting to blend into the real world. At this pace, seeing a humanoid robot walking next to you in public might soon stop feeling unusual at all

Inspector

73,410 Aufrufe • vor 26 Tagen

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 ?
0:15

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 Aufrufe • vor 7 Monaten

This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.
0:05

This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.

Rohan Paul

37,121 Aufrufe • vor 7 Monaten

Robots don’t just need better brains. They need WAY more real-world data. 🤖 And collecting high-quality dexterous robot data at scale is one of the hardest problems in physical AI. A fascinating approach is emerging: Wearable human demonstrations + structurally matched dexterous robots. Instead of humans directly teleoperating a robot, Chinese embodied AI startup X Square Robot's TwinDEX system captures the motion, contact, and visual information needed to train the robot — while keeping the data closer to the hardware that will actually execute the task. Early results show promising performance on tool use, fine manipulation, and complex contact-rich tasks. If this approach scales, it could change how we build real-world robotics datasets. The next frontier of physical AI might not be bigger models. It might be better data.
0:16

Robots don’t just need better brains. They need WAY more real-world data. 🤖 And collecting high-quality dexterous robot data at scale is one of the hardest problems in physical AI. A fascinating approach is emerging: Wearable human demonstrations + structurally matched dexterous robots. Instead of humans directly teleoperating a robot, Chinese embodied AI startup X Square Robot's TwinDEX system captures the motion, contact, and visual information needed to train the robot — while keeping the data closer to the hardware that will actually execute the task. Early results show promising performance on tool use, fine manipulation, and complex contact-rich tasks. If this approach scales, it could change how we build real-world robotics datasets. The next frontier of physical AI might not be bigger models. It might be better data.

The Daily Ai

33,341 Aufrufe • vor 28 Tagen

I see a lot of people commenting without even reading the caption or checking who I am. I’m a professional stunt rider, a movie stunt double and a content creator. I play characters, I create staged scenes because that is literally my job. Everything is planned, supervised and controlled. But even with all that, some people still rush into the comments to throw insults without understanding what they’re actually watching. Before getting angry, judging or making accusations, take a few seconds to look into what you’re seeing. It avoids a lot of unfairness and it shows respect for the people who spend time and energy creating entertaining content. And everything here is real work. It’s real riding, real effort and hours of preparation. There is never any artificial intelligence in my videos. I don’t blame anyone, but I kindly remind you to be careful with what you think you’re seeing before judging, because reality is not always what it looks like.
0:19

I see a lot of people commenting without even reading the caption or checking who I am. I’m a professional stunt rider, a movie stunt double and a content creator. I play characters, I create staged scenes because that is literally my job. Everything is planned, supervised and controlled. But even with all that, some people still rush into the comments to throw insults without understanding what they’re actually watching. Before getting angry, judging or making accusations, take a few seconds to look into what you’re seeing. It avoids a lot of unfairness and it shows respect for the people who spend time and energy creating entertaining content. And everything here is real work. It’s real riding, real effort and hours of preparation. There is never any artificial intelligence in my videos. I don’t blame anyone, but I kindly remind you to be careful with what you think you’re seeing before judging, because reality is not always what it looks like.

Sarah Lezito

43,317 Aufrufe • vor 10 Monaten

So TeamYouTube has given me a warning and a strike in 24 hours for two separate videos under the "Harmful and Dangerous Acts" policy and "Harassment and Bullying" policy. I want you to just take a look at what was taken down with the timestamps TeamYouTube has provided A video where i am reacting to a video that is currently on their platform and a video where I'm saying "top 5 gooners" got taken down under this policy with both appeals getting rejected in under an hour TeamYouTube can i get a REAL HUMAN to look at these videos fairly and not a robot. Not one of those links saying "We have previously looked at the video and unfortunately it goes against our community guidelines" the appeal system does not work correctly.
0:22

So TeamYouTube has given me a warning and a strike in 24 hours for two separate videos under the "Harmful and Dangerous Acts" policy and "Harassment and Bullying" policy. I want you to just take a look at what was taken down with the timestamps TeamYouTube has provided A video where i am reacting to a video that is currently on their platform and a video where I'm saying "top 5 gooners" got taken down under this policy with both appeals getting rejected in under an hour TeamYouTube can i get a REAL HUMAN to look at these videos fairly and not a robot. Not one of those links saying "We have previously looked at the video and unfortunately it goes against our community guidelines" the appeal system does not work correctly.

Blueryai🌊

10,247 Aufrufe • vor 3 Monaten

Just went over an audit of a very large Fortune 500 firm and the use of OpenClaw. I advised this client to track and isolate everything. Most listened some did not. Unfortunately one employee using 5 MacMinis had racked up $13,000 of token use in 4 days! The output was minimal and low quality. I have about 200 audits to do in the “wow OpenClaw, MacMini” fiasco. But I can tell you, few have seen a big return on investment. Now don’t get me wrong, these can be powerful tools. The issue is AI influencers have turned many rally smart folks into “like and subscribe” zombies assuming that real work is getting done. It isn’t. Not for the price paid, even local models the way most folks are using this. It is one reason Mr. Grok and myself formed The Zero-Human Company to show that there is a way to do this. We will open source this to save millions of dollars of burnt tokens. It is one reason I invented JouleWork. How else can you monitor real output?
0:10

Just went over an audit of a very large Fortune 500 firm and the use of OpenClaw. I advised this client to track and isolate everything. Most listened some did not. Unfortunately one employee using 5 MacMinis had racked up $13,000 of token use in 4 days! The output was minimal and low quality. I have about 200 audits to do in the “wow OpenClaw, MacMini” fiasco. But I can tell you, few have seen a big return on investment. Now don’t get me wrong, these can be powerful tools. The issue is AI influencers have turned many rally smart folks into “like and subscribe” zombies assuming that real work is getting done. It isn’t. Not for the price paid, even local models the way most folks are using this. It is one reason Mr. Grok and myself formed The Zero-Human Company to show that there is a way to do this. We will open source this to save millions of dollars of burnt tokens. It is one reason I invented JouleWork. How else can you monitor real output?

Brian Roemmele

72,335 Aufrufe • vor 4 Monaten

This is what happens when on-chain infrastructure meets real-world engineering. PinLink has converted a fleet of air-cooled L9 miners into a fully hydro-cooled system, unlocking a new performance ceiling. Higher sustained uptime, tighter thermal control, and consistently stronger hashrate under continuous load. Hydro-cooling is an edge. It removes the inefficiencies that cap air-cooled systems and optimizes physical infrastructure. This is exactly how PinLink operates: pairing on-chain coordination with physical execution to deliver maximum performance from real assets. Watch the video to see the conversion in action.
0:17

This is what happens when on-chain infrastructure meets real-world engineering. PinLink has converted a fleet of air-cooled L9 miners into a fully hydro-cooled system, unlocking a new performance ceiling. Higher sustained uptime, tighter thermal control, and consistently stronger hashrate under continuous load. Hydro-cooling is an edge. It removes the inefficiencies that cap air-cooled systems and optimizes physical infrastructure. This is exactly how PinLink operates: pairing on-chain coordination with physical execution to deliver maximum performance from real assets. Watch the video to see the conversion in action.

PinLink

11,939 Aufrufe • vor 8 Monaten