@m_wulfmeier
Mastery-first Physical AI @NomagicAI Prev. AI & Robotics @GoogleDeepMind, PhD/Postdoc @UniOfOxford, visiting @UCBerkeley @ETH @MIT, European @ELLISforEurope
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A post calls it one of FAR's most surprising recent projects and links to the collaborators' work.
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A post calls for simpler RL and welcomes what its author sees as momentum for critic-only approaches.
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The author says their write-up details how simulation complements real deployment in mastery-first physical AI.
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A poster sharing Mila's notice asks applicants interested in working with them to mention their name in the application.
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An MLinPL attendee says 800 engineers and researchers gathered, and argues physical AI is more nuanced than social-media portrayals.
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The speaker praised Warsaw’s tech talent and invited people to ask where they’re hiring before the talk.
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An attendee thanked ETH AI Center for the invitation and said the conference brought “many surprises.”
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An attendee representing the NFAI jury at SPRIND’s AI & Robotics Summit in Berlin says the challenge teams make them hopeful about robotics and future SPRIND challenges.
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One post predicts that big models will soon challenge the idea that they’re too slow for robotics. A quote-post argues that most such systems need little on-the-fly reasoning for real-time behavior.
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A contributor to “Visual Storytelling: An Embodied Companion” calls expert feedback on the drawing robots eye-opening, emphasizing the value of learning through deployment.
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LeRobot says the dataset spans 1,274 hours recorded in 27 homes and is available under a CC-BY-4.0 license.
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An ICLR submitter cites the conference’s blog tally of 19,525 submissions at the start of the previous cycle. A reply raises concerns that faster AI-assisted research encourages quantity over depth.
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Figure says its robots began useful work in 30 rented Bay Area homes without additional training. A team member reports 56% zero-shot success with Index pretraining, versus 8% without it.
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A speaker says they will discuss physical AI and the importance of deployment at the summit on October 1, 2026.
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A user hopes for robotics training that needs less data, but doubts that a better training objective will look very different from next-token prediction.
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A post credits the DARPA Robotics Challenge with enabling progress and floats another contest, arguing that handling objects remains hard and specialized setups often win.
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The visiting speaker says ETH’s Reliable ML team hosted them for a talk covering “mastery-first physical AI” and what reliability means after deployment.
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The post points to harness-design research as a way to improve performance, highlighting Graph-as-Policy (GaP), which it says was accepted to CoRL.
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A post describes Gemini guiding Gemini Robotics in EXIMO, a Google DeepMind student project, before the robot model learns to imitate that behavior.
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A post describes EXIMO as a Google DeepMind student research project that wraps Gemini around Gemini Robotics to provide human-interpretable, hierarchical control.
