Bryan Lim

76 posts

Bryan Lim

Bryan Lim

@bryanlimwt

Building agents and robot models @eastworlds_io @virtuals_io. Prev @autodesk ai lab, PhD @imperialcollege, MS @imperialcollege @MIT. AI/ML/Robotics.

Katılım Şubat 2021
254 Takip Edilen1K Takipçiler
Bryan Lim
Bryan Lim@bryanlimwt·
Excited to share a little bit of what I’ve been up to recently. Earlier this year, I left Autodesk after an incredible time developing foundation models for AEC. I've gone back to working with robots and have been building @eastworlds_io with an amazing team! Robotics is a notoriously unforgiving space. Slower feedback loops, hardware breakdowns, and the open-ended chaos of the physical world is painful. But that pain is what makes seeing a robot autonomously operate, recover and reliably work in the real world even more magical (even if it’s just picking up a bottle!). Eastworlds is building at the intersection of real-world deployment, data, models, and hardware. In particular, we're interested in rapid fuss-free robot deployments and high quality robot data from those real-world deployments. This simple model trained to reliably pick up bottles demonstrates our evaluation setup and validation of the data collected on the deployment and data platform we have been working on. We're privileged to work with early deployment partners in Asia who are equally excited about robots and being part of building the future and pushing the frontier of general-purpose robots and AI models that operate in the physical world. Looking forward to sharing more soon. If you’d like to chat or are interested in working together (esp. if you want real-world data or want to deploy and evaluate your robot models in the real-world on real use-cases), reach out!
Eastworlds@eastworlds_io

At Eastworlds, we validate our robot data before we sell it. Here, a Unitree G1 autonomously and reliably picks up a bottle using a model trained on Eastworlds' data with just $200 of compute.

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Bryan Lim
Bryan Lim@bryanlimwt·
Turning right becomes moving straight, going straight means turning left. This work is our attempt at addressing this through rapid online learning. Regaining control of the robot again and operating and driving it as if nothing was wrong felt magical! 2/3
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Bryan Lim
Bryan Lim@bryanlimwt·
Super happy to share that our work on robot adaptation is now out in @NatureComms! Imagine a robot being paralysed in some areas from unknown failures like a faulty or damaged wheel or motor, operating and driving such a vehicle is near impossible. 🧵1/3
Maxime Allard@allardmaxime079

🤖Thrilled to share that robotics work from my PhD is out in @NatureComms 🎉 "Getting robots back on track by reconstituting control in unexpected situations with online learning" With @MFlageat , @bryanlimwt , @CULLYAntoine @imperialcollege 🧵below

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Antoine Cully
Antoine Cully@CULLYAntoine·
Almost exactly 10 years after joining @imperialcollege as a Postdoc, I am honoured to announce that I am now Professor in Machine Learning and Robotics! 👨‍🎓 🤖 My fantastic team found the best gift to celebrate this special occasion!
Antoine Cully tweet media
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Bryan Lim
Bryan Lim@bryanlimwt·
Compared to other QD algorithms which use a few solutions from the archive for the next offspring (i.e. parents for a mutation/crossover or the init of an ES mean dist.), In-context QD makes greater use of the quality-diverse solutions in the archive for solution generation
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Bryan Lim
Bryan Lim@bryanlimwt·
💡⚙️ Ideas and inventions are rarely generated and created in isolation 🏝️ We explore using few/many-shot prompting of language models with quality-diverse examples provided from QD algorithms for solution generation for QD problems! Work done with @MFlageat @CULLYAntoine
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Bryan Lim
Bryan Lim@bryanlimwt·
It’ll be right after the Euro finals ⚽ so come on over after all the excitement/joy/disappointment. See you there! Room 112 if you’re in-person in Melbourne!
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Bryan Lim
Bryan Lim@bryanlimwt·
We believe there are still many things that all the different ideas and sub-fields of evolution can bring to RL and vice-verca, so the goal of this tutorial is to really encourage that 🚀🚀🚀
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Bryan Lim
Bryan Lim@bryanlimwt·
Excited to be presenting this year’s edition of Evolutionary Reinforcement Learning (EvoRL) @GeccoConf 🦎🦾 with @MFlageat @CULLYAntoine, which will be a tutorial instead of a workshop! 🧵
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Bryan Lim
Bryan Lim@bryanlimwt·
Very happy to share that I passed my PhD viva yesterday! Extremely grateful to @CULLYAntoine who has been a great advisor and mentor throughout this journey. Thank you for giving me this opportunity of a lifetime!
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Jenny Zhang
Jenny Zhang@jennyzhangzt·
@bryanlimwt @CULLYAntoine Congratulations Bryan!!! Excited to see what else you do Adding to your fun fact, Antoine went through C++ code with me line by line when I first joined
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