Max Simchowitz @max_simchowitz
Assistant Professor @mldcmu. Formerly: Postdoc @MITEECS, PhD @Berkeley_EECS, Math Undergrad @Princeton. New to Twitter. https://t.co/67bMOAyqK6 Joined May 2024-
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👏👏This is pretty massive!! Generative modeling looks clean in math, but getting it up and running can require a fair bit of alchemy. 🧪🧪 Thankfully, Nick Boffi (co-creator of Stochastic Interpolants) just dropped a super-clean, super-fast, super-reproducible repo for core…
👏👏This is pretty massive!! Generative modeling looks clean in math, but getting it up and running can require a fair bit of alchemy. 🧪🧪 Thankfully, Nick Boffi (co-creator of Stochastic Interpolants) just dropped a super-clean, super-fast, super-reproducible repo for core…
TRI's latest Large Behavior Model (LBM) paper landed on arxiv last night! Check out our project website: toyotaresearchinstitute.github.io/lbm1/ One of our main goals for this paper was to put out a very careful and thorough study on the topic to help people understand the state of the…
Very cool! In addition to optimizing inference-time search as a learning desideratum, this really speaks to power of building reward models purely from expert trajectories, via discriminative objectives. Excited to see how far this can go!
Very cool! In addition to optimizing inference-time search as a learning desideratum, this really speaks to power of building reward models purely from expert trajectories, via discriminative objectives. Excited to see how far this can go!
I am giving a talk "From Sim2Real 1.0 to 4.0 for Humanoid Whole-Body Control and Loco-Manipulation" at the RoboLetics 2.0 workshop @ieee_ras_icra today, summarizing my recent thoughts on sim2real. If you are interested: 2pm, May 23 @ room 302.
Want to scale robot data with simulation, but don’t know how to get large numbers of realistic, diverse, and task-relevant scenes? Our solution: ➊ Pretrain on broad procedural scene data ➋ Steer generation toward downstream objectives 🌐 steerable-scene-generation.github.io 🧵1/8
RL and post-training play a central role in giving language models advanced reasoning capabilities, but many algorithmic and scientific questions remain unanswered. Join us at FoPT @ COLT '25 to explore pressing emerging challenges and opportunities for theory to bring clarity.
RL and post-training play a central role in giving language models advanced reasoning capabilities, but many algorithmic and scientific questions remain unanswered. Join us at FoPT @ COLT '25 to explore pressing emerging challenges and opportunities for theory to bring clarity.
The Chicago Pope implies the existence of a New Keynesian Pope and a Behavioral Pope
really excited about this one -- please submit your best work, and come join us in beautiful Lyon to talk about machine learning and science!
really excited about this one -- please submit your best work, and come join us in beautiful Lyon to talk about machine learning and science!
Congrats to Andrea Bajcsy (@andrea_bajcsy) on receiving the NSF CAREER award! 👏 Her work: “Formalizing Open World Safety for Interactive Robots,” explores how robots make safe decisions beyond collision avoidance. Read about it and her education plans: loom.ly/59evuD0
Building AI systems is now a fragmented process spanning multiple organizations & entities. In new work (w/ @aspenkhopkins @cen_sarah @andrew_ilyas @imstruckman @LVidegaray), we study the implications of these emerging networks → what we call *AI supply chains* 🧵
Before the (exciting) workshops on Sun, catch Vincent’s oral talk at the #ICLR2025 main conference on this paper today at 3:30pm, Hall 1 Apex! And don’t forget to talk with the co-leads Vincent and @YiSu37328759 at the poster 10 a.m - 12:30 p.m Hall 3 + Hall 2B #558.
Before the (exciting) workshops on Sun, catch Vincent’s oral talk at the #ICLR2025 main conference on this paper today at 3:30pm, Hall 1 Apex! And don’t forget to talk with the co-leads Vincent and @YiSu37328759 at the poster 10 a.m - 12:30 p.m Hall 3 + Hall 2B #558.
So excited for this!!! The key technical breakthrough here is that we can control joints and fingertips of the robot **without joint encoders**. Learning from self-supervised data collection is all you need for training the humanoid hand control you see below.
So excited for this!!! The key technical breakthrough here is that we can control joints and fingertips of the robot **without joint encoders**. Learning from self-supervised data collection is all you need for training the humanoid hand control you see below.
Harman Marshal Singh @HarmanMarshal
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omid @omid_2015p
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Boyuan Chen @BoyuanChen0
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Vincent Sitzmann @vincesitzmann
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Noam Brown @polynoamial
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CMU Robotics Institut... @CMU_Robotics
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