Light Origins brings foundation-model intelligence into the physical world so machines can perceive, reason, and act.
Joined July 2026
- Tweets97
- Following19
- Followers1.3K
- Likes59
Pinned Tweet
Introducing Light-O1, a whole-body intelligence model scaled through human action pretraining.
• Human action at scale: We learn from structured human actions recovered from internet videos, capturing a scale and diversity that robot data collection alone struggles to match.
• Cross-embodiment transfer scaling law: Scaling human action pretraining yields power-law reductions in prediction error across embodiments.
• Whole-body intelligence: Adapted to different humanoids, Light-O1 coordinates movement, posture, and manipulation to autonomously perform household tasks.
Two humanoids. One home.
LightBot and Unitree G1 share a common starting point: Light-O1. Pretrained on human actions, then adapted to each robot.
A step toward robots becoming reliable partners in everyday life.
Nobody tells our robot when to vault and when to climb. It works that out on the way, from onboard sensing alone.
@ChemXiaodao and @Yuntao144 on how Light-Loco-Parkour learns from sparse seeds, with @micoolcho and @chris_j_paxton.
One of the key advantages of legged robots like humanoids should be how effectively they can move across a wide variety of terrain types to accomplish their task. But Light-Loco-Parkour from the team at Light Origins aims to change that: using only onboard sensing, they show a policy which can decide when to walk, vault, climb, or otherwise traverse as it moves through a complex environment. Unlike many others, it uses sparse seeds instead of relying on a large motion corpus, learning when to use its skills to move around without specific sub-task labels. @ChemXiaodao and @Yuntao144 join us to go into the details.
Watch Episode 104 of RoboPapers now, with @micoolcho and @chris_j_paxton, to learn more!
Robots won’t scale if every failure brings them to a halt.
We train a generalist Transformer policy across a wide range of hardware impairments, enabling it to adapt to the robot’s changing capabilities at runtime through whole-body in-context learning.
Meet Light REACT.
Read our full technical blog for details:lightorigins.com/en/blog/lig…
Any robot, any task, any scene. Amazing @LimX_Dynamics !
One Navigation Brain, Two LimX Platforms:
LightNav-0 × Oli / TRON 2 @LightOrigins’ open-source VLN model LightNav-0 was deployed zero-shot on LimX Dynamics’ Oli and TRON 2, enabling person following and instruction-based object search in real-world environments.
Open models. Open platforms. More possibilities for developers.
#LimXDynamics #LightOrigins #VLN #EmbodiedAI
LightOrigins retweeted
One Navigation Brain, Two LimX Platforms:
LightNav-0 × Oli / TRON 2 @LightOrigins’ open-source VLN model LightNav-0 was deployed zero-shot on LimX Dynamics’ Oli and TRON 2, enabling person following and instruction-based object search in real-world environments.
Open models. Open platforms. More possibilities for developers.
#LimXDynamics #LightOrigins #VLN #EmbodiedAI
Thanks to @DuinoDu, you can now tell Microduck where to go in plain English—and it just does it.
Two days ago, when we open-sourced our generalist navigation brain LightNav-0, we said: “Any robots, any tasks, any scenes.”
Running in sim today. Coming to your setup when it ships.
@pollenrobotics @huggingface
Code: github.com/lightorigins/Ligh…
Model: huggingface.co/LightOriginsH…
Paper: arxiv.org/abs/2608.30935
Tech blog: lightorigins.com/blog/lightn…
Discord: discord.gg/y9baPt4Ns
Introducing LightNav-0, our first general-purpose navigation brain. Open-sourced starting today.
Trained entirely in simulation, so it scales.
See scalable real2sim2real transfer across robots, tasks, and scenes.
Behind LightNav-0 is our scalable Real2Sim2Real data engine. It turns 2,000+ internet-sourced real-world scenes into 4,000+ hours of diverse navigation experience in simulation.
And it continues to scale.
Read our full technical blog for details:
lightorigins.com/en/blog/lig…
At Light Origins, we believe the path toward physical AGI runs through three scaling paradigms: scalable pre-training, scalable alignment, and scalable deployment.
With LightNav-0, our first step toward scalable alignment through Real2Sim2Real, we demonstrate that alignment at scale is key to achieving zero-shot generalization in the physical world.
Code: github.com/lightorigins/Ligh…
Model: huggingface.co/LightOriginsH…
