Introducing Real-time RL.
In the real world, time isn't free. The environment keeps "moving" even when you're computing your next action.
We show how RL agents can learn to adaptively think in real-time games.
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Check out our new work! @larsankile led this amazing project, and I learned so much about how we can use deployment data to efficiently train robust policies. It’s a really practical guide, and anyone training policies should definitely give it a read.
I've been thinking a lot about making robot learning scalable. To me, that means learning on the job, and using the human help still needed efficiently.
In Mulligan ⛳️, we focus each data-collection round on the initial states where the robot fails. This happens in deployment,
@perryadong has some awesome thoughts on how we can bring the scale and stability of RL post-training that we see in LLMs to large robotics models! Check it out
What will be the “RLHF” moment for robotics? What will it take to get robotics to where LLMs are today and beyond?
New blog post with @chelseabfinn sharing some thoughts on the state of RL for frontier robotics models and what's missing 👇
Blog: pd-perry.github.io/posts/post-tra…
Introducing Waddle Labs: Claude Code for robots.
Connect our API to your robot and enter a prompt, then our agents write code to achieve the task in 20 minutes.
@yiding_song@theWaddleLabs
