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Nicklas Hansen

@ncklashansen • 2,599 subscribers

Postdoc @CILVRatNYU. Prev: PhD @UCSanDiego, intern @nvidia, @MetaAI, @UCBerkeley, BS/MS @DTU_Compute. I like robots 🤖, plants 🪴, and they/them pronouns 🏳️‍🌈

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Super excited to share the last paper of my PhD: "Hallucination in World Models is Predictable and Preventable"✨ We train a 350M-param generative world model on a large dataset w/ 210 tasks and show that we can predict *when* hallucination happens and use that to fix it! 🧵1/n

Super excited to share the last paper of my PhD: "Hallucination in World Models is Predictable and Preventable"✨ We train a 350M-param generative world model on a large dataset w/ 210 tasks and show that we can predict when hallucination happens and use that to fix it! 🧵1/n

50,250 Aufrufe • vor 10 Tagen

🥳Excited to share: Hierarchical World Models as Visual Whole-Body Humanoid Controllers Joint work with Jyothir S V Vlad Sobal Yann LeCun Xiaolong Wang Hao Su Our method, Puppeteer, learns high-dim humanoid policies that look natural, in an entirely data-driven way! 🧵👇(1/n)

🥳Excited to share: Hierarchical World Models as Visual Whole-Body Humanoid Controllers Joint work with Jyothir S V Vlad Sobal Yann LeCun Xiaolong Wang Hao Su Our method, Puppeteer, learns high-dim humanoid policies that look natural, in an entirely data-driven way! 🧵👇(1/n)

250,912 Aufrufe • vor 2 Jahren

🚨 Announcing TD-MPC2: Scalable, Robust World Models for Continuous Control TD-MPC2 performs 100+ tasks without tuning, and allows us to train a single 317M parameter model to perform 80 tasks across multiple domains, embodiments, and action spaces! 🧵1/

🚨 Announcing TD-MPC2: Scalable, Robust World Models for Continuous Control TD-MPC2 performs 100+ tasks without tuning, and allows us to train a single 317M parameter model to perform 80 tasks across multiple domains, embodiments, and action spaces! 🧵1/

92,962 Aufrufe • vor 2 Jahren

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