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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.

Skild AI

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308,754 views • 1 month ago •via X (Twitter)

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Show it a single human demonstration of a long-horizon task and the robot performs it right away. No fine-tuning, no retraining. Skild has released S1, a robot foundation model where you specify the task with a video demonstration instead of a language instruction. Why video, not language Language works for "hand me the mug." It falls apart for anything delicate or long-horizon. You don't learn to fold a fitted sheet from a sentence. So S1 takes a video demo into its context window and translates it to its own body and scene. What it does - Tasks up to 10 minutes, dozens of sequential steps - Unseen tasks: potting plants, pour-over coffee, kit assembly, pancakes. Skild says even the underlying primitives, like flipping a pancake, weren't in pre-training - Recovers from its own mistakes without being trained to - Improves on flawed demos. Demonstrator drops an egg, S1 doesn't make that mistake. It reads the demo as a goal, not a trajectory to copy.

Show it a single human demonstration of a long-horizon task and the robot performs it right away. No fine-tuning, no retraining. Skild has released S1, a robot foundation model where you specify the task with a video demonstration instead of a language instruction. Why video, not language Language works for "hand me the mug." It falls apart for anything delicate or long-horizon. You don't learn to fold a fitted sheet from a sentence. So S1 takes a video demo into its context window and translates it to its own body and scene. What it does - Tasks up to 10 minutes, dozens of sequential steps - Unseen tasks: potting plants, pour-over coffee, kit assembly, pancakes. Skild says even the underlying primitives, like flipping a pancake, weren't in pre-training - Recovers from its own mistakes without being trained to - Improves on flawed demos. Demonstrator drops an egg, S1 doesn't make that mistake. It reads the demo as a goal, not a trajectory to copy.

The Humanoid Hub

19,034 views • 1 month ago

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:

Skild AI

3,553,322 views • 1 month ago

The robot flipped a pancake nobody taught it! 🥞 Skild AI team assumed pancake flipping had to be somewhere in the training data. So they searched. Millions of hours of pre-training data. Nothing. S1 inferred the whole task from a single human demonstration. That's their new general robot model, built as an in-context learner from the ground up. Every new robot task today starts with days of teleoperation and a fine-tuning run on a specialist policy. S1 skips all of it. Much like a language model, it never updates its weights to learn a new task. The demonstration enters the context window, and the policy uses it to decide what to do next. → Ten-minute tasks it was never trained on, composed from primitives learned in pre-training: a new style of coffee, potting a plant, frying pancakes. → Soil and pots arrived at their office at 8:54 PM. The robot was running the task autonomously by 9:27 PM. → Slide objects away mid-reach, swap them, change the lighting, it still finishes. → The prompt waters a plant with a watering can, but only a cup is available. It uses the cup. It doesn't rigidly replay what it saw, but it recovers from its own errors, and sometimes executes with more precision than the demonstrator, when the human fumbles an egg and makes a mess, S1 performs the same step cleanly. The demonstration is a specification of the goal, and not a trajectory to copy. On unseen tasks after 100K hours of pre-training: language-prompted VLAs reach 9%. Their new model reaches 66%. It's already deploying with industrial partners, with a wider rollout over the coming months. Congrats Deepak Pathak and team behind this! 😮‍💨 🔗 Link to their latest blog: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

The robot flipped a pancake nobody taught it! 🥞 Skild AI team assumed pancake flipping had to be somewhere in the training data. So they searched. Millions of hours of pre-training data. Nothing. S1 inferred the whole task from a single human demonstration. That's their new general robot model, built as an in-context learner from the ground up. Every new robot task today starts with days of teleoperation and a fine-tuning run on a specialist policy. S1 skips all of it. Much like a language model, it never updates its weights to learn a new task. The demonstration enters the context window, and the policy uses it to decide what to do next. → Ten-minute tasks it was never trained on, composed from primitives learned in pre-training: a new style of coffee, potting a plant, frying pancakes. → Soil and pots arrived at their office at 8:54 PM. The robot was running the task autonomously by 9:27 PM. → Slide objects away mid-reach, swap them, change the lighting, it still finishes. → The prompt waters a plant with a watering can, but only a cup is available. It uses the cup. It doesn't rigidly replay what it saw, but it recovers from its own errors, and sometimes executes with more precision than the demonstrator, when the human fumbles an egg and makes a mess, S1 performs the same step cleanly. The demonstration is a specification of the goal, and not a trajectory to copy. On unseen tasks after 100K hours of pre-training: language-prompted VLAs reach 9%. Their new model reaches 66%. It's already deploying with industrial partners, with a wider rollout over the coming months. Congrats Deepak Pathak and team behind this! 😮‍💨 🔗 Link to their latest blog: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

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Vision-language models perform diverse tasks via in-context learning. Time for robots to do the same! Introducing In-Context Robot Transformer (ICRT): a robot policy that learns new tasks by prompting with robot trajectories, without any fine-tuning. [1/N]

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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

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17,721 views • 8 months ago

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