Bryan Lim

PhD Student @imperialcollege. AI/ML/Robotics.

[Bryan Lim @bryanlimwt]

Massive effort from amazing collaborators @allardmaxime079 @MFlageat and @CULLYAntoine. Super thankful for the opportunity and privilege to play a part in this work!

3/3

[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

[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

[Bryan Lim @bryanlimwt]

@CULLYAntoine @imperialcollege Congratulations! 🎉

[Bryan Lim @bryanlimwt]

@vitalisvos19 Congratulations Vitalis!

[Bryan Lim @bryanlimwt]

More information in this 🧵 too!

https://t.co/2wOSoxz6KL

[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

[Bryan Lim @bryanlimwt]

Had a great time at @ALifeConf this week presenting In-context Quality-Diversity! Feel free to reach out if any of this is interesting #ALIFE2024

[Hiroki Sayama @HirokiSayama]

Bryan Lim discusses LLMs as an in-context quality-diversity generator
#ALIFE2024 @ALifeConf

[Bryan Lim @bryanlimwt]

Check out more in the paper!
📝: https://t.co/QfCIQHDs4t

[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

[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

[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!

[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!

🧵

[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 🚀🚀🚀

[Bryan Lim @bryanlimwt]

@v_vassiliades Thank you Vassilis!

[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!

[Bryan Lim @bryanlimwt]

@jennyzhangzt Thanks Jenny!!

[Bryan Lim @bryanlimwt]

@nrkcv Thanks Nemanja!!

[Bryan Lim @bryanlimwt]

@_samvelyan Thank you Mikayel!!