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Ge Yan
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Ge Yan

@GeYan_21
CS PhD Student @UW | Previously @UCSanDiego | Intern @ToyotaResearch @allen_ai
Seattle, WA
geyan21.github.io
Joined July 2019
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  • Pinned
    user avatar
    Ge Yan
    @GeYan_21
    Aug 12
    Are VLAs dead? No, and you don't have to choose between VLAs and WAMs. Introducing Flex-π: a multi-stream world-action model (WAM) that jointly predicts future RGB, 3D pointmaps and DINO semantics with actions in training, then deploys as a VLA, a full WAM, or anything in
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  • user avatar
    Ge Yan
    @GeYan_21
    Aug 23
    Thanks for the thoughtful comment. We fully agree that predicting future sensory states is indeed a world-model/dynamics objective, which is exactly why we call Flex-π in the paper a world-action model (WAM). In the post, we wanted to emphasize that our model can behave like a
    user avatar
    Jitendra MALIK
    @JitendraMalikCV
    Aug 23
    Nice idea to train to predict future sensory inputs along with action sequences. @ir413 Ilija Radosavovic et al (NeurIPS 2024) showed this for humanoid locomotion but the idea is perfectly general proceedings.neurips.cc/paper_files/pa… PS: Calling these models VLAs is a stretch.
  • user avatar
    Ge Yan
    @GeYan_21
    Aug 20
    Flex-π is now open source. 💻 github.com/geyan21/flex-pi 🤗 huggingface.co/flex-pi Code, checkpoints, and all our real-robot data from the five bimanual YAM tasks. One checkpoint runs every mode, action-only to full joint. Large-scale pre-training checkpoints coming next. Give it
    user avatar
    Ge Yan
    @GeYan_21
    Aug 12
    Are VLAs dead? No, and you don't have to choose between VLAs and WAMs. Introducing Flex-π: a multi-stream world-action model (WAM) that jointly predicts future RGB, 3D pointmaps and DINO semantics with actions in training, then deploys as a VLA, a full WAM, or anything in
    Image
    00:00
  • user avatar
    Ge Yan
    @GeYan_21
    Aug 12
    Thank you Yixin! We were also really impressed when we first saw Flex-π successfully handle these high-precision and dexterous manipulation tasks. It was exciting to see how capable it is.
    user avatar
    Yixin Zhang
    @yzhang4179
    Aug 12
    Impressive to see world-action modeling help on tasks this hard — long-horizon, contact-rich bimanual manipulation with sub-mm precision. Flex-π makes a strong case that predicting the future can improve robot behavior when the task gets hard. Amazing work @GeYan_21 and team! 🔥
  • user avatar
    Ge Yan
    @GeYan_21
    Mar 26
    Try it out! Great hardware for anyone who wants to do tactile sensing.
    user avatar
    Binghao Huang
    @binghao_huang
    Mar 25
    🤲Tactile sensing is powerful for robot manipulation, but hardware is still difficult to access, reproduce, and scale. 🎯That’s why we built FlexiTac: an open-source, low-cost, and scalable tactile sensing solution designed for real robotic systems. • Project page:
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