@ken_goldberg

Congrats @willjhliang, @JasonMa2020 @dineshjayaraman and collaborators on this cool combination of VLMs, keypoint detectors, and GOFE trajectory warping to self-supervise and reset 1000s of diverse pick-and-place demonstrations that are then used to train a VLA policy. https://t.co/OzEp63lx99

— Ken Goldberg (@Ken_Goldberg) 5 mars 2026

Congrats @willjhliang, @JasonMa2020 @dineshjayaraman and collaborators on this cool combination of VLMs, keypoint detectors, and GOFE trajectory warping to self-supervise and reset 1000s of diverse pick-and-place demonstrations that are then used to train a VLA policy. Will Liang (@willjhliang) Introducing Tether 🪢, a fun little idea to scale data by having our robot “play” in the real world for over 24 hours, throughout the day and overnight—improving policies from zero to mastery with minimal supervision! But play is messy, with out-of-distribution scenarios that are hard to anticipate. To perform autonomous functional play in the real world, from just a handful of demos, we propose a highly robust few-shot imitation method that warps demo trajectories using visual correspondences. Then, continuously running it within a multi-task VLM-guided cycle, we generate a data stream that produces 1000+ expert-level demos. This generated data is finally funneled downstream to train imitation learning policies, which improve from zero to near-perfect success rates. We’ll be presenting Tether at #ICLR2026 in just a few weeks! But before that, deep dive with me… 🧵 — https://nitter.net/willjhliang/status/2029238456766087386#m

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