Daniel Ho

@itsdanielho

Project Prometheus. Previously 1X, Waymo, Google X

San Francisco, CA

itsdanielho.com

Joined October 2009

361 Following

1,890 Followers

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Daniel Ho
@itsdanielho
Mar 22
A personal update: After two years at 1X, I’m moving on to something new. I joined 1X to solve general-purpose robotics, through the lens of evaluation. We bet on humanoid world models early in 2024. I’m proud of our work showing how the 1X World Model can solve the offline
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Daniel Ho
@itsdanielho
Feb 10
Check out this @RoboPapers pod for an overview of the past year of our world model research @1x_tech! We're very excited about world model architectures to achieve truly generalizable robot policies and evaluators. NEO will be able to zero-shot tasks in homes, learn rapidly
RoboPapers
@RoboPapers
Feb 4
Every home is different. That means that to build a useful home robot, we must be able to perform zero-shot generalization on a wide range of tasks. Humanoid company @1x_tech has a solution: world models. 1X Director of Evaluations @itsdanielho joins us on RoboPapers to talk
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Daniel Ho
@itsdanielho
Feb 2
Get ready for the @1x_tech world model RoboPapers pod drop!
RoboPapers
@RoboPapers
Feb 2
Full episode dropping soon! Geeking out with @itsdanielho on 1X World Model 1x.tech/discover/world… Co-hosted by @micoolcho @chris_j_paxton
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Daniel Ho
@itsdanielho
Jan 13
World model based polices like 1XWM we shared yesterday enables preference feedback during post-training and also test-time compute, because the model generates interpretable state One of the unlocks from this new type of architecture below the headlines
Jack Monas
@JackMonas
Jan 13
One of many next steps at @1x_tech: preference learning for world-model-based policies. Given a generated starting frame, we can sample multiple video rollouts from our WM and use preference feedback to steer the model toward higher-quality behavior. This lets us fix policy
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Daniel Ho
@itsdanielho
Jan 12
Excited to share our latest work on world models as robot policies! NEO executes novel manipulation tasks from text prompts, deriving actions from text-conditioned video generation. We found strong alignment between world model generations and real rollouts, and sufficient
1X
@1x_tech
Jan 12
NEO’s Starting to Learn on Its Own
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