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How can we help *any* image-input policy generalize better? 👉 Meet PEEK 🤖 — a framework that uses VLMs to decide *where* to look and *what* to do, so downstream policies — from ACT, 3D-DA, or even π₀ — generalize more effectively! 🧵

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Jesse Zhang

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13,696 görüntüleme • 8 ay önce •via X (Twitter)

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Current robot policies often face a tradeoff: they're either precise (but brittle) or generalizable (but imprecise). We present ViTaL, a framework that lets robots generalize precise, contact-rich manipulation skills across unseen environments with millimeter-level precision. 🧵
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Current robot policies often face a tradeoff: they're either precise (but brittle) or generalizable (but imprecise). We present ViTaL, a framework that lets robots generalize precise, contact-rich manipulation skills across unseen environments with millimeter-level precision. 🧵

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🤖 How can robot policies zero-shot generalize to any new environment and any new object? Introducing our new project: 🚀Data Scaling Laws in Imitation Learning for Robotic Manipulation🚀—bringing us closer to the dream of having robots work as waiters in hot pot restaurants! 🍲

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New research from BlackForestLabsAI - Unofficial 🥳 Meet Self-Flow: our self-supervised framework for image, audio, video & world models 🤖 Do generative models really need DINO to learn strong representations? We propose teaching them directly via a joint framework instead 🧵

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She broke DNA down. I keep telling you that Euro Americans created this narrative. Grok uses a study from 2015 to generalize African.
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Joe Mazzulla on how to bounce back from this loss: “Take a look at it. Figure out where you have to better, and put a full 48 minutes together. Have an understanding that it’s going to be difficult. That’s why we do what we do. Figure out where we have to get better and do that”

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New work with Alec Radford and David Duvenaud: Have you ever dreamed of talking to someone from the past? Introducing talkie, a 13B model trained only on pre-1931 text. Vintage models should help us to understand how LMs generalize (e.g., can we teach talkie to code?). Thread:

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Are black-box VLAs really the endgame for generalist robots? With SAGA, we explore an alternative path to efficiently generalize across environments, behaviors, and user specifications by explicitly grounding visual affordances in 3D space. 🧵

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Robotics fundamentally involves understanding the dynamics of how things change in the world in response to action and force. This is impossible to learn from static images; instead, it’s far more effective and more data-efficient to learn from video. Elvis Nava joins us to talk about Mimic Robotic. One of the key findings from mimic-video is that pretraining on webscale video allows robots to learn physics priors; as a result, policies train faster, generalize better, and are capable of more impressive dexterity, versus training on static images or image-language pairs as per a VLM. Watch Episode #81 of RoboPapers with Michael Cho - Rbt/Acc and Chris Paxton to learn more!

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S/O dark Kenny niggas !! We don’t generalize ! And no nigga who too much a coward to show his face can tell me shit !
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S/O dark Kenny niggas !! We don’t generalize ! And no nigga who too much a coward to show his face can tell me shit !

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Children learn to manipulate the world by playing with toys — can robots do the same? 🧸🤖 We show that robots trained on 250 "toys" made of 4 shape primitives (🔵,🔶,🧱,💍) can generalize grasping to real objects. Jitendra MALIK trevordarrell Shankar Sastry Berkeley AI Research😊

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Hindi lahat ng Carmelle supporters toxic. We’ve been RESPECTFUL, SUPPORTIVE, and KIND, not just to Carmelle, but even to Krystal. So don’t generalize us just to justify a questionable move. You KNOW the impact of votes. And yet you chose to do that and had the audacity+
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Hindi lahat ng Carmelle supporters toxic. We’ve been RESPECTFUL, SUPPORTIVE, and KIND, not just to Carmelle, but even to Krystal. So don’t generalize us just to justify a questionable move. You KNOW the impact of votes. And yet you chose to do that and had the audacity+

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I built software to cut docker image sizes by more than 80%. It uses dynamic analysis and and has a LOT more room for improvement. The demo here shows an ETL workflow docker image, cut down from 1.28GB to 193MB (-85%)! If large docker image sizes are becoming a production problem, I'd be happy to explore how we can help.

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If you let VLMs experiment on their own, they can do surprising things! From an image, we let a VLM code a 3D scene from scratch in Blender, and then render to verify/refine in a loop. Even when each step is imperfect, it gets results like this, with zero training.

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