Day 9/30: Learning RL in Robotics
Today, I'm proud to release my new research: FAACT (Failure-Aware Action Chunking Transformer).
Through lots of testing and design, I was able to create an embedding space for my model to understand failure states and exploit ACT to self-correct its motion.
Today, I demoed this at @newsystems_ to @join_ef and @KrishivThakuria, @thenadsusanto
More to come in the next few days at @socraticainfo...
Life Update: I've moved to SF to build the future of robotics learning @bracketbot with @sincethestudy.
My research focuses on unlocking continual learning for robotics policies.
More soon.
Every ANN method either gets slower as your corpus grows or starts retrieving the wrong things.
We built one that does neither.
50K vectors โ 10 ยตs / query
1M vectors โ 10 ยตs / query
5M vectors โ 10 ยตs / query
Introducing MeshRAG: retrieve anything with the same speed, same accuracy, no matter how much data you have.
Here is how we did it ๐
People talk, listen, watch, think, and collaborate at the same time, in real time. We've designed an AI that works with people the same way.
We share our approach, early results, and a quick look at our model in action.
https://t.co/AFJZ5kH7Ku