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The cool thing about talking with Bernt is that he isn’t just a robotics CEO, he’s a deep AI thinker who’s been obsessed with this for decades. It's obvious to me that this is his life's purpose. While most robotic labs are chasing narrow, pre-programmed behaviors, 1X built a... dynamics-first world model trained on video + robot sensors. It lets NEO generalize to completely new tasks out of the box, and get smarter through every failure - the same way humans do. Bernt says NEO doesn’t need to have seen every chore. It needs a World Model with enough human experience to imagine what should happen, a body dexterous enough to attempt it, and a real-world feedback loop sharp enough to correct it. That’s how we go from task automation to general-purpose labor.show more

Roberto Nickson

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World Labs co-founder Justin Johnson on how five photos from your phone could teach a robot to work in a room it has never seen: "I think there's a couple different tech trees that people are working on. One is this notion of fully in-context learning. Maybe I've got a robotics foundation model, then I can demonstrate to the robot once how a task should be performed, and that's enough for the robot to figure out that task." "Another version is what we're calling real-to-sim-to-real. Maybe I've got my pre-trained robotics foundation model, but I want to adapt it to this particular environment." "Like this studio... You could come in here and take just a couple casual videos with your phone or a couple images with your phone, use Atlas to reconstruct the space, and now stage all kinds of robotics interactions in this studio space in particular." "Literally you could do it in five minutes. Take five photos, upload it to Atlas, have it generate a simulation, describe in natural language what kind of task you want the robot to do, have an agent go build that simulation for you, RL fine-tune your general purpose robotics foundation model, and now you've got a robot... that's perfectly adapted to this space." Justin Johnson on MTS

World Labs co-founder Justin Johnson on how five photos from your phone could teach a robot to work in a room it has never seen: "I think there's a couple different tech trees that people are working on. One is this notion of fully in-context learning. Maybe I've got a robotics foundation model, then I can demonstrate to the robot once how a task should be performed, and that's enough for the robot to figure out that task." "Another version is what we're calling real-to-sim-to-real. Maybe I've got my pre-trained robotics foundation model, but I want to adapt it to this particular environment." "Like this studio... You could come in here and take just a couple casual videos with your phone or a couple images with your phone, use Atlas to reconstruct the space, and now stage all kinds of robotics interactions in this studio space in particular." "Literally you could do it in five minutes. Take five photos, upload it to Atlas, have it generate a simulation, describe in natural language what kind of task you want the robot to do, have an agent go build that simulation for you, RL fine-tune your general purpose robotics foundation model, and now you've got a robot... that's perfectly adapted to this space." Justin Johnson on MTS

a16z

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World Labs co-founder Justin Johnson predicts a real-to-sim-to-real future where Atlas could train a robot for any new environment in minutes using just 5 photos from your phone: "One is this notion of fully in-context learning. Maybe I've got a robotics foundation model, then I can demonstrate the robot once how a task should be performed, and that's enough for the robot to figure out." "Another version is real to sim to real. Maybe I've got my pre-trained robotics foundation model, but I want to adapt it to this particular environment, like this studio, this table, moving this microphone around." "There's a version where you could come in here and take just a couple casual videos with your phone or a couple images with your phone, use Atlas to reconstruct the space, and now stage all kinds of robotics interactions in this studio space." "If you could lower the barrier to doing that, you could do it in five minutes to take five photos, upload it to Atlas, have it generate a simulation, maybe describe in natural language what kind of task you want the robot to do, have an agent go build that simulation for you, RL fine-tune your general purpose robotics foundation model, and now you've got a robot maybe in the span of a couple minutes that could come in and is perfectly adapted to this space." World Labs

World Labs co-founder Justin Johnson predicts a real-to-sim-to-real future where Atlas could train a robot for any new environment in minutes using just 5 photos from your phone: "One is this notion of fully in-context learning. Maybe I've got a robotics foundation model, then I can demonstrate the robot once how a task should be performed, and that's enough for the robot to figure out." "Another version is real to sim to real. Maybe I've got my pre-trained robotics foundation model, but I want to adapt it to this particular environment, like this studio, this table, moving this microphone around." "There's a version where you could come in here and take just a couple casual videos with your phone or a couple images with your phone, use Atlas to reconstruct the space, and now stage all kinds of robotics interactions in this studio space." "If you could lower the barrier to doing that, you could do it in five minutes to take five photos, upload it to Atlas, have it generate a simulation, maybe describe in natural language what kind of task you want the robot to do, have an agent go build that simulation for you, RL fine-tune your general purpose robotics foundation model, and now you've got a robot maybe in the span of a couple minutes that could come in and is perfectly adapted to this space." World Labs

MTS

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🤖What if a robot could perform a new task just from a natural language command, with zero demonstrations? Our new work, NovaFlow, makes it possible! We use pre-trained video generative model to create a video of the task, then translate it into a plan for real-world robot execution. 1/6 #Robotics #AI #ZeroShot #Manipulation

🤖What if a robot could perform a new task just from a natural language command, with zero demonstrations? Our new work, NovaFlow, makes it possible! We use pre-trained video generative model to create a video of the task, then translate it into a plan for real-world robot execution. 1/6 #Robotics #AI #ZeroShot #Manipulation

Hongyu Li

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Engram cofounder Jack Morris just raised $98M to build a new type of AI. He says models don't need to get smarter over time. Instead, they just need to know you better and better over time. Jack describes what he's building: "Our product is a new type of AI. We have a pretty different vision from a lot of the frontier labs, which are working on one model per lab, and trying to make that model smarter every month." "There's another way to think about it, which is that the model doesn't need to get smarter every month. It needs to know you better." "So we're working on a whole different stack, which is a way to train models that train themselves to know your world better and adjust to the things that you say." "So: new ways of training, new ways of running the models."

Engram cofounder Jack Morris just raised $98M to build a new type of AI. He says models don't need to get smarter over time. Instead, they just need to know you better and better over time. Jack describes what he's building: "Our product is a new type of AI. We have a pretty different vision from a lot of the frontier labs, which are working on one model per lab, and trying to make that model smarter every month." "There's another way to think about it, which is that the model doesn't need to get smarter every month. It needs to know you better." "So we're working on a whole different stack, which is a way to train models that train themselves to know your world better and adjust to the things that you say." "So: new ways of training, new ways of running the models."

TBPN

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I do not know how we got here to a place where this is even something we need to speak about, but we are here and we have to be real! Enough is enough ! Enough with the lies ! Enough with the trying to create something that doesn’t need to exist! men are men women are women THATS IT.

I do not know how we got here to a place where this is even something we need to speak about, but we are here and we have to be real! Enough is enough ! Enough with the lies ! Enough with the trying to create something that doesn’t need to exist! men are men women are women THATS IT.

Tish Hyman

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The World Model as NEO's Cognitive Core 1X has revealed a major AI development where the NEO humanoid can translate any natural language prompt into robotic action. It demonstrates this capability even for novel tasks, objects, and environments not found in its robot dataset. - the 1X World Model is trained on internet-scale human interaction videos and fine-tuned with robot data to ground its understanding in physics and in NEO's embodiment - from a simple voice or text prompt, the world model generates a visualization of future actions - a built-in inverse dynamics model then translates these into precise motor movements for NEO

The World Model as NEO's Cognitive Core 1X has revealed a major AI development where the NEO humanoid can translate any natural language prompt into robotic action. It demonstrates this capability even for novel tasks, objects, and environments not found in its robot dataset. - the 1X World Model is trained on internet-scale human interaction videos and fine-tuned with robot data to ground its understanding in physics and in NEO's embodiment - from a simple voice or text prompt, the world model generates a visualization of future actions - a built-in inverse dynamics model then translates these into precise motor movements for NEO

The Humanoid Hub

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15–30 minutes of real-world robot data. That's now enough to go from sim-to-real failure to working robot. Let’s see… You train a robot in simulation. You deploy it in the real world. It fails. The physics don't match. So you try to fine-tune it with real data, but… you never have enough real data, and the fine-tuning breaks everything the simulation taught it. SimDist fixes this with one key decision: don't transfer the policy. Transfer the world model. Keep the reward and value knowledge from simulation frozen. Only update the part that's actually wrong, how the robot predicts physics. Now the robot doesn't have to relearn the entire task in the real world. It already knows what success looks like. It just needs to correct its understanding of how the real world moves. The part that makes this work: they also trained on failures and recoveries; not just perfect demonstrations. Without that, the planner finds the gaps and exploits them. With it, the robot can tell a good future from a bad one. That's all it needs. Results on peg insertion, table leg assembly, locomotion on slippery and uneven surfaces. Tasks that require precision, force, and quick reaction. Thanks for sharing, Tyler Westenbroek ([Tyler Westenbroek]. Interactive visualization + paper: ——- Weekly robotics and AI insights. Subscribe free:

15–30 minutes of real-world robot data. That's now enough to go from sim-to-real failure to working robot. Let’s see… You train a robot in simulation. You deploy it in the real world. It fails. The physics don't match. So you try to fine-tune it with real data, but… you never have enough real data, and the fine-tuning breaks everything the simulation taught it. SimDist fixes this with one key decision: don't transfer the policy. Transfer the world model. Keep the reward and value knowledge from simulation frozen. Only update the part that's actually wrong, how the robot predicts physics. Now the robot doesn't have to relearn the entire task in the real world. It already knows what success looks like. It just needs to correct its understanding of how the real world moves. The part that makes this work: they also trained on failures and recoveries; not just perfect demonstrations. Without that, the planner finds the gaps and exploits them. With it, the robot can tell a good future from a bad one. That's all it needs. Results on peg insertion, table leg assembly, locomotion on slippery and uneven surfaces. Tasks that require precision, force, and quick reaction. Thanks for sharing, Tyler Westenbroek ([Tyler Westenbroek]. Interactive visualization + paper: ——- Weekly robotics and AI insights. Subscribe free:

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Every home is different. That means that to build a useful home robot, we must be able to perform zero-shot generalization on a wide range of tasks. Humanoid company 1X has a solution: world models. 1X Director of Evaluations Daniel Ho joins us on RoboPapers to talk about: - why world models are the future for scaling robot learning - how to use world models for robot control - what world models unlock for evaluating robot model performance - how we can hill-climb from here to general purpose robots Watch Episode #61 of RoboPapers, with Michael Cho - Rbt/Acc and Chris Paxton, now!

Every home is different. That means that to build a useful home robot, we must be able to perform zero-shot generalization on a wide range of tasks. Humanoid company 1X has a solution: world models. 1X Director of Evaluations Daniel Ho joins us on RoboPapers to talk about: - why world models are the future for scaling robot learning - how to use world models for robot control - what world models unlock for evaluating robot model performance - how we can hill-climb from here to general purpose robots Watch Episode #61 of RoboPapers, with Michael Cho - Rbt/Acc and Chris Paxton, now!

RoboPapers

27,904 görüntüleme • 7 ay önce

We set out to collect 10M hours of on-embodiment data as a starting point for robotics models, but couldn’t find a robot reliable enough at a price that made it possible. So, we built one. Meet Pandroid: an accessible hardware platform for AI. Small enough to safely experiment with, durable enough to run in fleets. Connect it to any model or coding agent over SSH to let it interact with the world. Join the waitlist:

We set out to collect 10M hours of on-embodiment data as a starting point for robotics models, but couldn’t find a robot reliable enough at a price that made it possible. So, we built one. Meet Pandroid: an accessible hardware platform for AI. Small enough to safely experiment with, durable enough to run in fleets. Connect it to any model or coding agent over SSH to let it interact with the world. Join the waitlist:

Pantograph

126,015 görüntüleme • 1 ay önce

SITUATION EXPLAINED: A new generation of AI labs are betting that world models are the path to general intelligence that can interact with the real world. We had General Intuition CEO Pim de Witte on MTS to explain what world models are, and why intuitively world models offer a clearer path towards general intelligence than LLMs. For humans, text is a technology for compressing information about the unfolding, three-dimensional world around us and communicating it to other people. When LLMs reason about the world, they do so by reconstructing the world through text. World models work by modeling the three-dimensional world at the architecture level and then allowing agents to interact with that world. Unlike an LLM, which understands space and time through text, world models perform spatial and temporal reasoning natively. General Intuition hopes to use world models to create embodied AI—machines that can reason and act in the real world. But other companies are also pushing the frontier in this space. Odyssey ML today announced a multi-agent world model, Agora-1. Their launch video shows four participants interacting with a real-time world model simulation of GoldenEye, a first-person shooter for the N64. A playable preview of the GoldenEye simulation is available on their site.

SITUATION EXPLAINED: A new generation of AI labs are betting that world models are the path to general intelligence that can interact with the real world. We had General Intuition CEO Pim de Witte on MTS to explain what world models are, and why intuitively world models offer a clearer path towards general intelligence than LLMs. For humans, text is a technology for compressing information about the unfolding, three-dimensional world around us and communicating it to other people. When LLMs reason about the world, they do so by reconstructing the world through text. World models work by modeling the three-dimensional world at the architecture level and then allowing agents to interact with that world. Unlike an LLM, which understands space and time through text, world models perform spatial and temporal reasoning natively. General Intuition hopes to use world models to create embodied AI—machines that can reason and act in the real world. But other companies are also pushing the frontier in this space. Odyssey ML today announced a multi-agent world model, Agora-1. Their launch video shows four participants interacting with a real-time world model simulation of GoldenEye, a first-person shooter for the N64. A playable preview of the GoldenEye simulation is available on their site.

MTS

11,676 görüntüleme • 3 ay önce

Introducing FLUX-mimic, a next-generation Video-Action Model for general purpose dexterity, developed in partnership with Black Forest Labs. Late last year we published mimic-video and introduced Video-Action Models (VAM): a new family of robotics foundation models built on top of video generation models. We showed that robot control reduces to visual prediction, and that robot capability is downstream of improvements in video modeling accuracy. The obvious implication was that advances in the video modeling frontier would directly translate to increased capabilities in end-to-end robot learning. FLUX-mimic is that thesis at frontier scale: We've applied our VAM architecture to the strongest video backbone available today, FLUX 3 from Black Forest Labs, and trained it on data from our own robots and wearables. General-purpose dexterity, running on a single GPU on premises. Because the model already understands world dynamics, it needs far fewer demonstrations to learn a new task. This is game-changing for our mission to deploy robots to factory floors, where industrial robot data is scarce and expensive to collect. We're now testing and deploying FLUX-mimic with manufacturing leaders like Audi USA, on complex, multi-step manipulation long considered impossible for conventional automation.

Introducing FLUX-mimic, a next-generation Video-Action Model for general purpose dexterity, developed in partnership with Black Forest Labs. Late last year we published mimic-video and introduced Video-Action Models (VAM): a new family of robotics foundation models built on top of video generation models. We showed that robot control reduces to visual prediction, and that robot capability is downstream of improvements in video modeling accuracy. The obvious implication was that advances in the video modeling frontier would directly translate to increased capabilities in end-to-end robot learning. FLUX-mimic is that thesis at frontier scale: We've applied our VAM architecture to the strongest video backbone available today, FLUX 3 from Black Forest Labs, and trained it on data from our own robots and wearables. General-purpose dexterity, running on a single GPU on premises. Because the model already understands world dynamics, it needs far fewer demonstrations to learn a new task. This is game-changing for our mission to deploy robots to factory floors, where industrial robot data is scarce and expensive to collect. We're now testing and deploying FLUX-mimic with manufacturing leaders like Audi USA, on complex, multi-step manipulation long considered impossible for conventional automation.

mimic

118,172 görüntüleme • 1 ay önce

Elon Musk just said out loud what the entire robotics industry spent thirty years refusing to see. Musk: “Humanity has designed the world to interact with a bipedal humanoid with two arms and ten fingers.” Every staircase. Every doorknob. Every factory floor. Every hospital corridor. Civilization is not an open environment. It is a monument built around one specific body. The industry spent decades engineering quadrupeds, wheeled machines, exotic locomotion systems. Then spent more years asking how to retrofit the world around them. Tesla never asked that question. Musk: “If you want to have a robot fit in and be able to do things that humans can do, it must be of roughly the same size and shape and capability.” This is not a design philosophy. It is a pass key to every structure humanity has ever built. Optimus does not navigate civilization. It inherits it. Musk: “Work that is repetitive, boring, dangerous. Basically, work that people don’t want to do unless they’re paid to do it.” Remove every task performed out of obligation rather than choice. What remains is pure cognitive output. Not automation. The liberation of human attention at species scale. The interviewer pushed back directly: “I thought four legs are better.” Musk laughed. Musk: “Four legs good, two legs bad.” Someone in the room said exactly what the entire industry believed. He dismissed it with an Orwell joke and moved on. Whoever fields this first does not gain a labor advantage. They inherit the physical layer of civilization itself. Every structure ever built. Every tool ever forged. Every corridor ever designed. Already engineered for this exact body. The world was never neutral ground. It was always waiting for the right body to walk through it. Someone was always going to see it. Most just spent thirty years looking away.

Elon Musk just said out loud what the entire robotics industry spent thirty years refusing to see. Musk: “Humanity has designed the world to interact with a bipedal humanoid with two arms and ten fingers.” Every staircase. Every doorknob. Every factory floor. Every hospital corridor. Civilization is not an open environment. It is a monument built around one specific body. The industry spent decades engineering quadrupeds, wheeled machines, exotic locomotion systems. Then spent more years asking how to retrofit the world around them. Tesla never asked that question. Musk: “If you want to have a robot fit in and be able to do things that humans can do, it must be of roughly the same size and shape and capability.” This is not a design philosophy. It is a pass key to every structure humanity has ever built. Optimus does not navigate civilization. It inherits it. Musk: “Work that is repetitive, boring, dangerous. Basically, work that people don’t want to do unless they’re paid to do it.” Remove every task performed out of obligation rather than choice. What remains is pure cognitive output. Not automation. The liberation of human attention at species scale. The interviewer pushed back directly: “I thought four legs are better.” Musk laughed. Musk: “Four legs good, two legs bad.” Someone in the room said exactly what the entire industry believed. He dismissed it with an Orwell joke and moved on. Whoever fields this first does not gain a labor advantage. They inherit the physical layer of civilization itself. Every structure ever built. Every tool ever forged. Every corridor ever designed. Already engineered for this exact body. The world was never neutral ground. It was always waiting for the right body to walk through it. Someone was always going to see it. Most just spent thirty years looking away.

Dustin

420,301 görüntüleme • 6 ay önce

Tesla Optimus is showing increasingly human-like dexterity, one of the hardest things for any robot to master Walking is impressive, but hands are what make a humanoid truly useful Gripping, adjusting, picking up objects, handling tools, and interacting with the world the way humans do… that’s where real-world automation begins Optimus is being built for our world, designed to handle the kinds of tasks humans do every day

Tesla Optimus is showing increasingly human-like dexterity, one of the hardest things for any robot to master Walking is impressive, but hands are what make a humanoid truly useful Gripping, adjusting, picking up objects, handling tools, and interacting with the world the way humans do… that’s where real-world automation begins Optimus is being built for our world, designed to handle the kinds of tasks humans do every day

X Freeze

94,653 görüntüleme • 2 ay önce

OpenAI's Kevin Weil 🇺🇸 says 24/7 robotic labs could automate scientific discovery using "reinforcement learning with a loop through the real world": "There’s a lot of science that can be totally automated. There’s no reason at this point that you need to have grad students pipetting one thing into another thing." "The idea is to have robotic labs that are online 24/7 and can scale in parallel. You have models reasoning for two days to find the most efficient experiments to run, once they get to a good point, they pass that to a robotic lab which can experiment in parallel at high volume." "The results pass back into a model which reasons about the results and then goes out and runs a different set of experiments. You’re doing reinforcement learning with a loop through the real world."

OpenAI's Kevin Weil 🇺🇸 says 24/7 robotic labs could automate scientific discovery using "reinforcement learning with a loop through the real world": "There’s a lot of science that can be totally automated. There’s no reason at this point that you need to have grad students pipetting one thing into another thing." "The idea is to have robotic labs that are online 24/7 and can scale in parallel. You have models reasoning for two days to find the most efficient experiments to run, once they get to a good point, they pass that to a robotic lab which can experiment in parallel at high volume." "The results pass back into a model which reasons about the results and then goes out and runs a different set of experiments. You’re doing reinforcement learning with a loop through the real world."

TBPN

29,721 görüntüleme • 7 ay önce

OpenAI's Kevin Weil 🇺🇸 says 24/7 robotic labs could automate scientific discovery using "reinforcement learning with a loop through the real world": "There’s a lot of science that can be totally automated. There’s no reason at this point that you need to have grad students pipetting one thing into another thing." "The idea is to have robotic labs that are online 24/7 and can scale in parallel. You have models reasoning for two days to find the most efficient experiments to run, once they get to a good point, they pass that to a robotic lab which can experiment in parallel at high volume." "The results pass back into a model which reasons about the results and then goes out and runs a different set of experiments. You’re doing reinforcement learning with a loop through the real world."

OpenAI's Kevin Weil 🇺🇸 says 24/7 robotic labs could automate scientific discovery using "reinforcement learning with a loop through the real world": "There’s a lot of science that can be totally automated. There’s no reason at this point that you need to have grad students pipetting one thing into another thing." "The idea is to have robotic labs that are online 24/7 and can scale in parallel. You have models reasoning for two days to find the most efficient experiments to run, once they get to a good point, they pass that to a robotic lab which can experiment in parallel at high volume." "The results pass back into a model which reasons about the results and then goes out and runs a different set of experiments. You’re doing reinforcement learning with a loop through the real world."

TBPN

98,711 görüntüleme • 7 ay önce

Rogin: It looks very likely that we're about to restart bombing of Iran. And there are a lot of reports out there that this is about to happen. And what that means is that all of the problems, not just with the US economy, with the economy of every country in the world, are about to get worse. And it means that the Iran war is going to go much longer than otherwise anticipated. And that is a disaster not just for Americans, but for all people. And then if you think about the effects on energy and then manufacturing, and then rates will continue to go up, and then inflation, it's a it's a calamity for the world economy with no strategy whatsoever.

Rogin: It looks very likely that we're about to restart bombing of Iran. And there are a lot of reports out there that this is about to happen. And what that means is that all of the problems, not just with the US economy, with the economy of every country in the world, are about to get worse. And it means that the Iran war is going to go much longer than otherwise anticipated. And that is a disaster not just for Americans, but for all people. And then if you think about the effects on energy and then manufacturing, and then rates will continue to go up, and then inflation, it's a it's a calamity for the world economy with no strategy whatsoever.

Acyn

74,258 görüntüleme • 3 ay önce

Godfather of AI Yann LeCun reveals the humanoid robot industry's secret: the companies building the robots have no idea how to make them smart enough to be useful. "There is a lot of expectations that robotics is going to be a revolution, essentially, in a big market, eventually." "But the secret of the industry is that none of the companies at the moment that are building robots, like humanoid robots, none of those companies has any idea how to make those robots smart enough to be useful." "There's a big push for AI to make progress towards... understanding the physical world. That's what is prompting this investment." "There's nothing wrong with investing in LLMs. LLMs are useful. They're just addressing a completely different set of applications, really." LeCun is not describing someone else's problem. He has spent 15 years on world models, raised more than $1 billion for AMI Labs to build that missing intelligence layer, and in the same interview he calls 2026 the year of the world model and puts consumer robots 3 to 5 years out. Every humanoid robot demo you have seen is a body waiting for a brain. The winner in robotics will not be the company that shipped the body; it will be the one that ships the intelligence. - Yann LeCun (Yann LeCun), Turing Award winner and founder of AMI Labs, on Bloomberg Television.

Godfather of AI Yann LeCun reveals the humanoid robot industry's secret: the companies building the robots have no idea how to make them smart enough to be useful. "There is a lot of expectations that robotics is going to be a revolution, essentially, in a big market, eventually." "But the secret of the industry is that none of the companies at the moment that are building robots, like humanoid robots, none of those companies has any idea how to make those robots smart enough to be useful." "There's a big push for AI to make progress towards... understanding the physical world. That's what is prompting this investment." "There's nothing wrong with investing in LLMs. LLMs are useful. They're just addressing a completely different set of applications, really." LeCun is not describing someone else's problem. He has spent 15 years on world models, raised more than $1 billion for AMI Labs to build that missing intelligence layer, and in the same interview he calls 2026 the year of the world model and puts consumer robots 3 to 5 years out. Every humanoid robot demo you have seen is a body waiting for a brain. The winner in robotics will not be the company that shipped the body; it will be the one that ships the intelligence. - Yann LeCun (Yann LeCun), Turing Award winner and founder of AMI Labs, on Bloomberg Television.

Karl Mehta

44,534 görüntüleme • 1 ay önce

We are back. After one year of quiet building. Introducing GENE-26.5, our first robotic brain that takes a major step toward human-level capability. For years, robotics has struggled to learn from the world’s largest and valuable data source: Humans. Solving it means rethinking the whole stack from the ground up: - A robotics-native foundation model. - A 1:1 human-like robotic hand. - A noninvasive data collection glove for motion, force, and touch. - A simulator that turns weeks of experiments into minutes. GENE-26.5 is trained across language, vision, proprioception, tactile, and action. We designed a set of tasks to test how far we can go with this new paradigm. Fully autonomous, 1x speed, one model, same weights. (Enjoy with sound on) We are approaching the endgame for robotics. And this is just a beginning.

We are back. After one year of quiet building. Introducing GENE-26.5, our first robotic brain that takes a major step toward human-level capability. For years, robotics has struggled to learn from the world’s largest and valuable data source: Humans. Solving it means rethinking the whole stack from the ground up: - A robotics-native foundation model. - A 1:1 human-like robotic hand. - A noninvasive data collection glove for motion, force, and touch. - A simulator that turns weeks of experiments into minutes. GENE-26.5 is trained across language, vision, proprioception, tactile, and action. We designed a set of tasks to test how far we can go with this new paradigm. Fully autonomous, 1x speed, one model, same weights. (Enjoy with sound on) We are approaching the endgame for robotics. And this is just a beginning.

Genesis AI

2,737,967 görüntüleme • 4 ay önce