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Humanoid Behavior Foundation Models are entering the perception era. Perceptive BFM shows how vision can adapt the same human motion prior to different terrains instead of assuming the environment is already compatible. Really impressive work!

Eren Chen

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18,918 次观看 • 3 个月前 •via X (Twitter)

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this is how the additives are layered, allowing the same locomotion additives to work across different weapon poses. 🤔

this is how the additives are layered, allowing the same locomotion additives to work across different weapon poses. 🤔

Tector

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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

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

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Asking a fit person what they do to stay fit AND assuming that’s how they got fit is like asking a rich person how they maintain wealth and assuming that’s how they built it. The maintenance protocol and the building protocol are not the same thing. If you model the current behavior without the foundation, you’re solving the wrong problem. And yeah, the build takes some time… But once you get there? Maintenance is easy. If you want to start from the right place without trial and error, DM me “TRAIN”.

Asking a fit person what they do to stay fit AND assuming that’s how they got fit is like asking a rich person how they maintain wealth and assuming that’s how they built it. The maintenance protocol and the building protocol are not the same thing. If you model the current behavior without the foundation, you’re solving the wrong problem. And yeah, the build takes some time… But once you get there? Maintenance is easy. If you want to start from the right place without trial and error, DM me “TRAIN”.

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This is what the Bedrock Operator sees on a job site. The camera feed shows what a human operator would see. The LiDAR point cloud shows what the machine knows, the full geometry of the environment, rendered in real time, down to millimeter precision. The excavator's position, the bucket's angle, the shape of the cut: all of it mapped and updated continuously. This is the sensing foundation that makes autonomous operation possible. Before a machine can act safely and accurately, it has to understand its environment completely.

This is what the Bedrock Operator sees on a job site. The camera feed shows what a human operator would see. The LiDAR point cloud shows what the machine knows, the full geometry of the environment, rendered in real time, down to millimeter precision. The excavator's position, the bucket's angle, the shape of the cut: all of it mapped and updated continuously. This is the sensing foundation that makes autonomous operation possible. Before a machine can act safely and accurately, it has to understand its environment completely.

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A humanoid labeled Dragongwing IQ10 went down on stage and needed human intervention before the presentation could continue. That failure is useful. Balance recovery nnee torque floor contact restart behavior. These are the details that decide whether humanoids can work outside a booth. #HumanoidRobot #Robotics #PhysicalAI #AIRobotics

A humanoid labeled Dragongwing IQ10 went down on stage and needed human intervention before the presentation could continue. That failure is useful. Balance recovery nnee torque floor contact restart behavior. These are the details that decide whether humanoids can work outside a booth. #HumanoidRobot #Robotics #PhysicalAI #AIRobotics

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Once you have the visionOS SDK installed you can really see how the idea of light and dark mode just doesn't make sense with Vision Pro – click the mountain icon in the bottom-right corner to try different environments, and also different times of day 🌄

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Seedance 2.5 with the same prompt. Lovely and very much reminds me of the (overly) optimistic future of space books that used to be popular; predicting space tourism coming soon. (Also the reflections these models can pull off are impressive)

Seedance 2.5 with the same prompt. Lovely and very much reminds me of the (overly) optimistic future of space books that used to be popular; predicting space tourism coming soon. (Also the reflections these models can pull off are impressive)

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Life is not the same for all of us but we are all human. Why do they deserve different to your children your family?

Life is not the same for all of us but we are all human. Why do they deserve different to your children your family?

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NEURALINK: REWIRING THE FUTURE OF HUMAN PERCEPTION Neuralink begins by restoring sight. •⁠ ⁠It bypasses damaged eyes and speaks directly to the brain •⁠ ⁠Future applications may unlock vision beyond human capability •⁠ ⁠Interfaces designed to read and write neural signals in real time •⁠ ⁠Potential to restore mobility, memory, and sensory function Neural interfaces are transitioning from lab testing to human integration. A new era of brain-machine connection is underway. Source: Neuralink, Elon Musk

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$40 hours of human work. That’s what this humanoid could save every month! A construction company is already testing a Unitree G1 on a real job site, using the robot for site inspections, 360° imaging, data collection and progress monitoring. The robot starts at around $13,500, while the company says its deployment can save roughly 40 hours of work every month. That adds up to around 480 hours a year from a machine that costs a fraction of traditional industrial equipment. The interesting part is that this isn't about replacing an entire construction worker. It's about removing hundreds of repetitive hours from the workflow, including walking inspection routes, documenting progress and collecting information across the site. Humans can then spend their time on decisions and tasks that actually require them. This is where humanoid robots become economically interesting. Construction sites are already designed around human movement, so a robot with two arms, two legs and a human-sized body can potentially work in the same spaces without rebuilding the entire environment. Every additional task it learns turns those same hardware costs into more productive hours. And the economics get even more interesting as prices fall and production scales. A robot that saves 480 hours per year doesn't need to be perfect or replace a full-time employee to justify its existence. It just needs to reliably take over the boring, repetitive work that companies are already paying humans to do. 480 hours saved. Thousands of dollars in hardware. One construction site. This is how humanoids will enter the workforce, not by replacing everyone overnight, but by quietly taking over the hours nobody wants to spend.$

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A HUMANOID ROBOT JUST FOUND ONE OF THE MOST EXPENSIVE AISLES IN THE WORLD. An airplane cabin. Think about why this environment is so interesting. Narrow aisle. Human-sized equipment. Repetitive service. Hundreds of passengers. Thousands of flights every day. And almost everything inside the cabin was already designed around the dimensions of a human worker. That's exactly why the humanoid form matters. You don't redesign the aircraft. You build a machine capable of operating inside what already exists. But the bigger opportunity isn't necessarily replacing the crew. Imagine robots eventually handling repetitive cabin tasks while humans remain responsible for safety, judgment and passenger care. Drinks. Trash collection. Moving supplies. Routine cabin checks. Every small task removed is another minute of human labor redirected somewhere more valuable. That's how humanoid automation may actually arrive. Not: “This robot replaced five people.” But: “This robot removed 30% of the repetitive work from their shift.” Multiply that across an entire industry... and suddenly the boring little drink cart becomes a very interesting business case.

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