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Pierre Sermanet
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Pierre Sermanet
@psermanet
Co-Founder & Chief Scientist at UMA
sermanet.github.io
Joined November 2017
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  • user avatar
    Pierre Sermanet
    @psermanet
    Jun 17, 2019
    A major benefit of self-supervision is we can truly scale and adapt on the fly. It could be 10% behind supervised ImageNet, it would still do better in real life. We show in online-objects.github.io that the longer our model looks at objects, the better it understands them.
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    Pierre Sermanet
    @psermanet
    Nov 12, 2025
    After 11 incredible years at @Google Brain and @GoogleDeepMind, I’m turning the page to start something new in robotics (currently in stealth 👀). I joined Brain in 2014, drawn by the momentum around robotics and machine learning, and I’ll always be grateful to @V_Vanhoucke for
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    Pierre Sermanet
    @psermanet
    Apr 27, 2019
    My slides from the @OpenAI robotics symposium, the main message is self-supervision on lots of unlabeled play data is an effective recipe for robotics, and we propose multiple methods to implement this recipe for vision and control:
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    docs.google.com
    Self-Supervision and Play - Pierre Sermanet @ OpenAI Robotics Symposium 2019 (public, 45 mins)
    Self-Supervision and Play Pierre Sermanet In collaboration with Corey Lynch, Debidatta Dwibedi, Soeren Pirk, Jonathan Tompson, Mohi Khansari, Yusuf Aytar, Yevgen Chebotar, Yunfei Bai, Jasmine Hsu,...
  • user avatar
    Pierre Sermanet
    @psermanet
    Dec 1, 2017
    Our updated TCN results show that a real robot can learn a new task from a single human video after self-supervising on unlabeled videos. Details at sermanet.github.io/imitate
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    Pierre Sermanet
    @psermanet
    May 18, 2020
    We present a simple and scalable approach for controlling robots with natural language: play through teleoperation, then answer “how do I go from start to finish?” for random episodes. We can then type in commands in real time. Paper: language-play.github.io with @coreylynch
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    Pierre Sermanet
    @psermanet
    Nov 9, 2023
    Need help in the real world? RoboVQA can guide robots and humans through long-horizon tasks on a phone via Google Meet. We release a dataset of 800k (video, question/answer) with robots & humans doing various long-horizon tasks. Data: robovqa.github.io @GoogleDeepMind
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    Pierre Sermanet
    @psermanet
    Dec 3, 2018
    Give a robot a label and you feed it for a second; teach a robot to label and you feed it for a lifetime.
  • user avatar
    Pierre Sermanet
    @psermanet
    Mar 7, 2019
    How to scale-up multi-task learning? Self-supervise plan representations from lots of cheap unlabeled play data (no RL was used). learning-from-play.github.io by @coreylynch, Mohi Khansari, @xiao_ted, @Vikashplus, Jonathan Tompson, @svlevine and @psermanet
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    Corey Lynch
    Figure
    @coreylynch
    Mar 6, 2019
    Excited to share our new work on learning from play! We show a single agent, after self-supervising on 3 hours of play data, can generalize to 18 zero-shot manipulation tasks with 85% success. interactive paper: learning-from-play.github.io 1/
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  • user avatar
    Pierre Sermanet
    @psermanet
    May 21, 2018
    Our latest work on continuous control from pixels by @debidatta, Jonathan Tompson, @coreylynch, and I will be presented at the MLPC workshop at #ICRA2018 this afternoon in room M4. Paper: cs.unm.edu/amprg/Workshop…
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    Pierre Sermanet
    @psermanet
    Jun 15, 2019
    Come hear how to train the Cake at our workshop on Self-Supervised Learning today at ICML: sites.google.com/corp/view/self… Lineup: Jacob Devlin, Alison Gopnik, @coreylynch, @hendrycks, @chelseabfinn, @ylecun, @__kolesnikov__, Olivier Henaff A Zisserman, Abhinav Gupta, Alyosha Efros.
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    Pierre Sermanet
    @psermanet
    Jun 17, 2019
    Replying to @psermanet
    We present this paper today at the LUV and DeepVision workshops at #CVPR2019. Paper: online-objects.github.io Authors: @_pirk_, Mohi Khansari, Yunfei Bai, @coreylynch, @psermanet
  • user avatar
    Pierre Sermanet
    @psermanet
    Mar 13, 2025
    Q: How can we ensure robots behave properly at scale? A: Robot constitutions 📜! Q: How do we verify behavior in undesirable situations at scale? A: Generation! We release the ASIMOV Benchmark for Semantic Safety of robots at asimov-benchmark.github.io @GoogleDeepMind
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  • user avatar
    Pierre Sermanet
    @psermanet
    Dec 20, 2017
    Another great example of how far self-supervision can take us: learning to localize objects that make sounds, no labels required!
    user avatar
    Relja Arandjelović
    @relja_work
    Dec 20, 2017
    In our new work, "Objects that Sound", we train a network to localize the object that is making the sound, without any supervision whatsoever. See this video (youtube.com/watch?v=TFyohk…) for qualitative results and read the paper here arxiv.org/pdf/1712.06651
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    Pierre Sermanet
    @psermanet
    Jan 4, 2018
    Slides from my self-supervised imitation talk at #MLParis today. I sprinkled some cat videos in it to make it more interesting. More details available in the speaker notes: docs.google.com/presentation/d…
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