How can we train RL agents that transfer to any reward? In our NeurIPS Conference paper DiSPO, we propose to learn the distribution of successor features of a stationary dataset, which enables zero-shot transfer to arbitrary rewards without additional training! A thread 🧵(1/9)

Can't wait to see how this incredible tool changes the game for robotics and AI applications. 🚀

Have some offline data lying around? Use it to robustify few-shot imitation learning! 🤖 STRAP 🎒 is a retrieval-based method that leverages semantic sub-trajectories in offline datasets to augment the training data. 🧵 1/6

How can we continuously improve large pretrained behavior policies when 0-shot performance is not good enough? Directly finetuning the base policy via RL tends to be sample-inefficient. Can we squeeze more juice from the base policy to enable automatic and efficient performance

So you’ve trained your favorite diffusion/flow based policy, but it’s just not good enough 0-shot. Worry not, in our new work DSRL - we show how to *steer* pre-trained diffusion policies with off-policy RL, improving behavior efficiently enough for direct training in the real

🚨Tired of binary pass/fail metrics that miss the bigger picture? 🤖Introducing #RoboEval — an open benchmark that shows *how* robot manipulation policies behave and *why* they fail, not just *if* they succeed. 🧵1/n 🔗 robo-eval.github.io 📄 robo-eval.github.io/media/RoboEval…

I will be on an island in the Puget Sound this weekend, so sadly I will be missing #CoRL2025tv! But luckily the amazing students who did all the work anyways, will be 😄 Here's what the WEIRD lab at the University of Washington has going on at CoRL this time We'll be presenting

We will have few presentations, posters and live demo tomorrow. Come hang out and chat with me at anytime~ Oral 1 & Spotlight 1 & Poster 1 & Demo (day1)

How can we help *any* image-input policy generalize better to visual and semantic variations? 👉 Meet PEEK 🤖 — a framework that uses VLMs to decide *where* to look and *what* to do, so downstream policies — from ACT, 3D-DA, or even π₀ — generalize more effectively!