@gs_ai_ working on the best simulator for robotics! previously @CMU_Robotics @BerkeleyCDSS
bay area
Joined October 2022
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1/ Progress in robot locomotion scaled with simulation, but touch was left behind. Our work, Tactile Genesis, unlocks sim for dexterous manipulation. Using sim to ablate tactile types and models, we can quickly converge to better tactile hardware and force-informed policies.
The latest Genesis World v1.3.0 releases differentiable sim, better friction, and update for tactile sensors. Check it out! genesis-world.readthedocs.io…
Calibrating sim with real tactile sensors is still hard. We don't know yet if some other representations may be the answer!
Presenting PTLD: an approach to learning tactile dexterous policies without ever simulating the tactile sensor.
Tactile is essential for performing highly dexterous manipulation. However, collecting tactile observations reliably has been a fundamental bottleneck: (1) teleoperating a multi fingered hand for dynamic tasks is challenging, making sim-to-real imperative, (2) one can’t realistically simulate tactile today
Trinity Chung retweeted
Robotics people, follow @aran_nayebi . He's been asking some really fundamental questions about what morphologies we need for robotics, and comes from a really unique bio/neuro perspective. Check this work out!
It's clear that to unlock the next big advances in robotics, we need at-scale tactile sensing. For the past year, in collab w/ @gs_ai_, we've been working on perhaps the most wide-ranging, realistic tactile simulator to ask:
*What should the future of robot hands look like?*
8/ For a more complete discussion on my work in tactile sensing, my master's thesis presentation is available at youtube.com/watch?v=YP7WUNRm… 😄
9/ Thanks to my advisors @aran_nayebi @KaterinaFragiad and collaborators @kashu_yamazaki @gs_ai_ for supporting me throughout this work! ❤️ Follow them for more cutting edge NeuroAI and robotics research🧠🦾
Trinity Chung retweeted
🚀 New Open-Source Release! PyTorchTNN 🚀
A PyTorch package for building biologically-plausible temporal neural networks (TNNs)—unrolling neural network computation layer-by-layer through time, inspired by cortical processing. PyTorchTNN naturally integrates into the Encoder-Attender-Decoder (EAD) architecture (Chung*, Shen* et al., 2025), which flexibly combines diverse neural networks, motivated by the fact that no single model (Transformer, SSM, RNN) dominates all sequence learning tasks.
🧵👇
Trinity Chung retweeted
🤖 What if a humanoid robot could make a hamburger from raw ingredients—all the way to your plate?
🔥 Excited to announce ViTacFormer: our new pipeline for next-level dexterous manipulation with active vision + high-resolution touch.
🎯 For the first time ever, we demonstrate ~2.5 minutes of continuous, autonomous control—combining active vision, high-res touch, and high-DoF robot hands SharpaWave — to complete complex, real-world tasks.
Code is fully released; check out our:
Homepage: roboverseorg.github.io/ViTac…
Paper link: arxiv.org/abs/2506.15953
Github: github.com/RoboVerseOrg/ViTa…
Trinity Chung retweeted
I always found it puzzling how language models learn so much from next-token prediction, while video models learn so little from next frame prediction. Maybe it's because LLMs are actually brain scanners in disguise. Idle musings in my new blog post: sergeylevine.substack.com/p/…
i just got another notification from NSF GRFP and my result changed from honorable mention to awardee! i guess they got more money back?
Trinity Chung retweeted
Our first NeuroAgent! 🐟🧠
Excited to share new work led by the talented @rdkeller, showing how autonomous behavior and whole-brain dynamics emerge naturally from intrinsic curiosity grounded in world models and memory.
Some highlights:
- Developed a novel intrinsic drive (3M-Progress) that better matches the reliable autonomy of animals
- First task-optimized model of neural-glial computation
- Surprisingly, no linear regression needed: a simple 1-to-1 mapping was enough to pass the NeuroAI Turing Test on whole-brain zebrafish data (~130,000 recorded units), provided you have the right intrinsic drive of course!
Check it out! 👇
1/ I'm excited to share recent results from my first collaboration with the amazing @aran_nayebi and @Leokoz8!
We show how autonomous behavior and whole-brain dynamics emerge in embodied agents with intrinsic motivation driven by world models.
Trinity Chung retweeted
Robots need touch for human-like hands to reach the goal of general manipulation. However, approaches today don’t use tactile sensing or use specific architectures per tactile task.
Can 1 model improve many tactile tasks?
🌟Introducing Sparsh-skin: tinyurl.com/y935wz5c
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