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🤖 Introducing ω-0 (OMEGA-0) — a whole-body World Action Model for humanoids. Can one model make a humanoid walk, manipulate, and coordinate its whole body simultaneously across many real-world tasks? ω-0 does exactly that. ⚡ One unified model for multi-task whole-body loco-manipulation 🧠 Predicts future visual latents while generating... executable whole-body actions 🏠 Trained with ω-HOME: 40+ hours, 24 household tasks, 4,827 episodes 🔥 81.8% real-world success rate across 11 evaluation tasks No separate locomotion/manipulation policies. One model. Whole body. Multiple tasks. Humanoid intelligence should not be a collection of isolated skills. It should be a unified whole-body policy. 🔗 📜 One-take, uncut real-world demo👇show more

Jianfei Yang

1,360 subscribers

18,940 views • 1 month ago •via X (Twitter)

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4 Comments

Manpreet Bola1 month ago

81.8% is the headline. Table 2 also lists ψ-0 at 44.5% on the same 11 tasks. That is a 37.3 point cap on this board. Progress sits at 90.3% while full success is 81.8%. The model often moves the task and still fails the success criterion. Those are two different scores. *table 2*

Susan Grant 🥶🦍1 month ago

Terrified and wanting to put it on my Christmas wish list at the same time! 🤖😱🥳

Bhashwar Ghosh1 month ago

in omega-0 does the predicted visual latent actually feed the action head at inference, or is it only there in the loss during training

Anto Patrex1 month ago

81.8% across 11 household tasks is solid but the real question is what happens in environments that weren't in the training set. a warehouse with wet cardboard and shifting pallets is a different world than a home kitchen. does the world model generalize or does it memorize?

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624,910 views • 8 months ago

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11,089 views • 3 months ago

AGIBOT has unveiled AGILE (AgiBot Generative Intelligent Locomotion Engine), a perception-control foundation model for whole-body humanoid locomotion. - It fuses visual perception, balance, and motion planning end-to-end, replacing the traditional split between "seeing" and "moving." - The robot reads terrain and obstacles in real time and adjusts its gait with no preset trajectories. - It runs on local compute with millisecond-level response and works across AGIBOT's lineup (A1/A2/A3, X2). AGILE is the locomotion "cerebellum" that complements AGIBOT's Genie Operator "brain."

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14,499 views • 4 months ago

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179,696 views • 1 year ago

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Zhengyi “Zen” Luo

47,345 views • 2 years ago

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Zhengyi “Zen” Luo

62,714 views • 10 months ago

Holy… is this really not a human? That was my reaction too,I could hardly believe it. This is an ultra-humanlike humanoid robot. 1.73 m tall, 70 kg, with 115 degrees of freedom across the body,36 in the hands alone,and full-body tactile skin. … This is KAI, the first humanoid robot from Shenzhen-based KinetixAI. It’s essentially a 1:1 human replica，except it has no face. I wonder if it could use Aheadform’s M1? that will be interesting. Powered by its KAI World Model and trained on large-scale Engocentric data, it can fold clothes, use tools, handle deliveries and takeout, and assist with childcare,covering a wide range of household tasks. Ok, expectations for Tesla Optimus v3 have been raised once again.

Holy… is this really not a human? That was my reaction too,I could hardly believe it. This is an ultra-humanlike humanoid robot. 1.73 m tall, 70 kg, with 115 degrees of freedom across the body,36 in the hands alone,and full-body tactile skin. … This is KAI, the first humanoid robot from Shenzhen-based KinetixAI. It’s essentially a 1:1 human replica，except it has no face. I wonder if it could use Aheadform’s M1? that will be interesting. Powered by its KAI World Model and trained on large-scale Engocentric data, it can fold clothes, use tools, handle deliveries and takeout, and assist with childcare,covering a wide range of household tasks. Ok, expectations for Tesla Optimus v3 have been raised once again.

CyberRobo

122,304 views • 5 months ago

This work is not about a new technique. GMT (General Motion Tracking) shows good engineering practices that you can actually train a single unified whole-body control policy for all agile motion, and it works in the real world, directly with sim2real without adaptation. This is different from many existing works that trains/adapts one individual policy for each specific motion. Instead of just showing the beautiful motions/dances, this is actually practical. Because now you can convert all types of commands into real actions in real time without tuning anything. Visit to check out how we handle the following challenges: ✅ Partial Observability. ✅ Hardware Limitations. ✅ Unbalanced Data Distribution. ✅ Model Expressiveness. Work led by Zixuan Chen Mazeyu Ji

This work is not about a new technique. GMT (General Motion Tracking) shows good engineering practices that you can actually train a single unified whole-body control policy for all agile motion, and it works in the real world, directly with sim2real without adaptation. This is different from many existing works that trains/adapts one individual policy for each specific motion. Instead of just showing the beautiful motions/dances, this is actually practical. Because now you can convert all types of commands into real actions in real time without tuning anything. Visit to check out how we handle the following challenges: ✅ Partial Observability. ✅ Hardware Limitations. ✅ Unbalanced Data Distribution. ✅ Model Expressiveness. Work led by Zixuan Chen Mazeyu Ji

Xiaolong Wang

83,347 views • 1 year ago