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Skild AI

@SkildAI • 23,677 subscribers

Building general purpose robotic intelligence.

Shorts

It was born in a physics simulation. For 140 years, our model played against increasingly tough opponents, each one a previous version of itself.

It was born in a physics simulation. For 140 years, our model played against increasingly tough opponents, each one a previous version of itself.

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Robotics is a data problem. Today, we’re partnering with ABB Robotics, Universal Robots, and NVIDIA Robotics to deploy the Skild Brain across real-world industries from manufacturing to factory lines. This will help us build the world’s biggest data flywheel for physical AI.

Robotics is a data problem. Today, we’re partnering with ABB Robotics, Universal Robots, and NVIDIA Robotics to deploy the Skild Brain across real-world industries from manufacturing to factory lines. This will help us build the world’s biggest data flywheel for physical AI.

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Thanks to Nasdaq for celebrating our 100M ARR milestone in Times Square.

Thanks to Nasdaq for celebrating our 100M ARR milestone in Times Square.

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What makes it so resilient? It's "omni-bodied". The Skild Brain spent 1,000 years walking 100,000 different bodies across simulated worlds. In our world, the Brain treats a broken robot as just another body.

What makes it so resilient? It's "omni-bodied". The Skild Brain spent 1,000 years walking 100,000 different bodies across simulated worlds. In our world, the Brain treats a broken robot as just another body.

68,276 просмотров

Imagine Mars rovers that can adapt to broken wheels. Submersibles that can finish critical repairs with faulty thrusters. A rescue robot with crushed legs that can keep saving lives.

Imagine Mars rovers that can adapt to broken wheels. Submersibles that can finish critical repairs with faulty thrusters. A rescue robot with crushed legs that can keep saving lives.

30,639 просмотров

Happy Holidays!! 🎄🎄

Happy Holidays!! 🎄🎄

24,991 просмотров

Videos

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We trained a robot to play football. How? Self-play for 140 years in a virtual World Cup. Meet the #Messinator:

We trained a robot to play football. How? Self-play for 140 years in a virtual World Cup. Meet the #Messinator:

1,773,163 просмотров • 29 дней назад

Introducing S1, our new foundation model that learns from one example. It can be taught 10-minute long tasks that it has never seen before, from one video prompt without any fine-tuning. Watch S1 operate in real-time via in-context learning:

Introducing S1, our new foundation model that learns from one example. It can be taught 10-minute long tasks that it has never seen before, from one video prompt without any fine-tuning. Watch S1 operate in real-time via in-context learning:

3,556,644 просмотров • 1 месяц назад

We just hit 100M ARR within 10 months of starting deployments. We are in factory lines. On construction sites. In kitchens. In data centers. Cleaning. Welding. Building. Cooking. Deploying.

We just hit 100M ARR within 10 months of starting deployments. We are in factory lines. On construction sites. In kitchens. In data centers. Cleaning. Welding. Building. Cooking. Deploying.

1,684,048 просмотров • 1 месяц назад

It can be taught extremely long-horizon tasks, over 10 minutes long. By composing skills learned in pretraining or creating new ones, it can make coffee, pot a plant, fry pancakes, and more – tasks never seen during pretraining.

It can be taught extremely long-horizon tasks, over 10 minutes long. By composing skills learned in pretraining or creating new ones, it can make coffee, pot a plant, fry pancakes, and more – tasks never seen during pretraining.

338,238 просмотров • 1 месяц назад

We built a robot brain that nothing can stop. Shattered limbs? Jammed motors? If the bot can move, the Brain will move it— even if it’s an entirely new robot body. Meet the omni-bodied Skild Brain:

We built a robot brain that nothing can stop. Shattered limbs? Jammed motors? If the bot can move, the Brain will move it— even if it’s an entirely new robot body. Meet the omni-bodied Skild Brain:

2,445,111 просмотров • 1 год назад

The first time S1 flipped a pancake, we assumed pancake flipping must have been in its pre-training data. We searched our whole pre-training data and found no examples of flipping. S1 inferred the out-of-distribution task from one video prompt.

The first time S1 flipped a pancake, we assumed pancake flipping must have been in its pre-training data. We searched our whole pre-training data and found no examples of flipping. S1 inferred the out-of-distribution task from one video prompt.

315,964 просмотров • 1 месяц назад

S1 learns new tasks like a language model. You prompt it with a video demonstration, and it outputs robot actions to complete the task in any environment and in any embodiment.

S1 learns new tasks like a language model. You prompt it with a video demonstration, and it outputs robot actions to complete the task in any environment and in any embodiment.

308,754 просмотров • 1 месяц назад

Compared to conventional VLAs, S1 is a step-change improvement. For known tasks, S1 can match the performance of language-prompted VLAs. For novel tasks, S1 exponentially outperforms any existing language-prompted VLAs as we scale the pre-training. We believe this is a promising direction towards establishing scaling laws for robotics.

Compared to conventional VLAs, S1 is a step-change improvement. For known tasks, S1 can match the performance of language-prompted VLAs. For novel tasks, S1 exponentially outperforms any existing language-prompted VLAs as we scale the pre-training. We believe this is a promising direction towards establishing scaling laws for robotics.

302,433 просмотров • 1 месяц назад

S1 does not blindly replay the video demonstration. Instead, it displays common-sense understanding that goes beyond the video prompt: - can withstand perturbations - improvise upon mistakes, even if the human didn’t - at times execute with more precision than the human in the video prompt

S1 does not blindly replay the video demonstration. Instead, it displays common-sense understanding that goes beyond the video prompt: - can withstand perturbations - improvise upon mistakes, even if the human didn’t - at times execute with more precision than the human in the video prompt

300,400 просмотров • 1 месяц назад

We found that to match the accuracy that S1 can achieve with just one example of prompting, current VLA models would need to be post-trained with 50-100 hours of data collection followed by fine-tuning!

We found that to match the accuracy that S1 can achieve with just one example of prompting, current VLA models would need to be post-trained with 50-100 hours of data collection followed by fine-tuning!

295,652 просмотров • 1 месяц назад

Once it was ready, we brought it to life.

Once it was ready, we brought it to life.

113,607 просмотров • 29 дней назад

Humans learn by watching. Robots should too.

Humans learn by watching. Robots should too.

1,078,089 просмотров • 9 месяцев назад

Nearly every system today, from energy to chips to food, is bottlenecked by scarce human capital. We are changing that by building AI-powered industries of the future. Check out Skild Brain robustly assembling GPU racks, a highly precise task, live at #NvidiaGTC.

Nearly every system today, from energy to chips to food, is bottlenecked by scarce human capital. We are changing that by building AI-powered industries of the future. Check out Skild Brain robustly assembling GPU racks, a highly precise task, live at #NvidiaGTC.

287,381 просмотров • 7 месяцев назад

Modern AI is confined to the digital world. At Skild AI, we are building towards AGI for the real world, unconstrained by robot type or task — a single, omni-bodied brain. Today, we are sharing our journey, starting with early milestones, with more to come in the weeks ahead. Our Mission: Artificial General Intelligence grounded in the physical world. We believe AGI that can truly understand and reason in the real world can only be built through grounding in the physical world. Our Vision: Any robot, Any task, One brain. We tackle robotics in its full generality – building a continually improving, omni-bodied brain that can control any hardware for any task. Who are we? A passionate group of scientists & engineers driven by our shared vision. We have been researching AI and robotics for more than a decade. Our team includes pioneers of self-supervised learning, curiosity-driven exploration, end-to-end sim2real for visual locomotion, dexterous manipulation, learning from human videos, robot parkour, and many more. Many of these works have won awards at top-tier AI and Robotics conferences. Our team has also built production-ready systems at Anduril, Tesla, Nvidia, Meta, Kitty Hawk, Google, Everyday Robotics, and Amazon. Join us in our mission to build the robot brains of tomorrow.

Modern AI is confined to the digital world. At Skild AI, we are building towards AGI for the real world, unconstrained by robot type or task — a single, omni-bodied brain. Today, we are sharing our journey, starting with early milestones, with more to come in the weeks ahead. Our Mission: Artificial General Intelligence grounded in the physical world. We believe AGI that can truly understand and reason in the real world can only be built through grounding in the physical world. Our Vision: Any robot, Any task, One brain. We tackle robotics in its full generality – building a continually improving, omni-bodied brain that can control any hardware for any task. Who are we? A passionate group of scientists & engineers driven by our shared vision. We have been researching AI and robotics for more than a decade. Our team includes pioneers of self-supervised learning, curiosity-driven exploration, end-to-end sim2real for visual locomotion, dexterous manipulation, learning from human videos, robot parkour, and many more. Many of these works have won awards at top-tier AI and Robotics conferences. Our team has also built production-ready systems at Anduril, Tesla, Nvidia, Meta, Kitty Hawk, Google, Everyday Robotics, and Amazon. Join us in our mission to build the robot brains of tomorrow.

386,618 просмотров • 1 год назад

India, we’re here! 🇮🇳🇺🇸 From one Silicon Valley to another, we’re thrilled to open our newest office in Bengaluru.

India, we’re here! 🇮🇳🇺🇸 From one Silicon Valley to another, we’re thrilled to open our newest office in Bengaluru.

204,730 просмотров • 8 месяцев назад

We’ve all seen humanoid robots doing backflips and dance routines for years. But if you ask them to climb a few stairs in the real world, they stumble! We took our robot on a walk around town to environments that it hadn’t seen before. Here’s how it works🧵⬇️

We’ve all seen humanoid robots doing backflips and dance routines for years. But if you ask them to climb a few stairs in the real world, they stumble! We took our robot on a walk around town to environments that it hadn’t seen before. Here’s how it works🧵⬇️

272,097 просмотров • 1 год назад

can't catch me now 🏃💨🪚🪚

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can't catch me now 🏃💨🪚🪚

222,388 просмотров • 1 год назад

Let robots clean in peace 🧺🤖✨

Let robots clean in peace 🧺🤖✨

170,721 просмотров • 9 месяцев назад

We’ve been building quietly — starting tomorrow, we go live. Here’s a teaser of what we did before Skild AI. It has shaped what’s coming next. 07/29. Stay tuned.

We’ve been building quietly — starting tomorrow, we go live. Here’s a teaser of what we did before Skild AI. It has shaped what’s coming next. 07/29. Stay tuned.

169,342 просмотров • 1 год назад

Jensen NVIDIA just gave our bot a Thor-level upgrade. Pretty sure it is worthy of Mjolnir now. 🔨⚡️

Jensen NVIDIA just gave our bot a Thor-level upgrade. Pretty sure it is worthy of Mjolnir now. 🔨⚡️

127,611 просмотров • 1 год назад