Pinned
Excited to introduce PolaRiS, a real-to-sim recipe for turning short real-world videos into high fidelity simulation environments for scalable and reliable zeroshot generalist policy evaluation.
polaris-evals.github.io
(1/N 🧵)
Arhan Jain
@prodarhan
beep boop 🤖 @uwcse
- amazing progress on real2sim evals 🤠Today, we're introducing SimFoundry, our real2sim2real framework at NVIDIA GEAR that automatically turns real-world scenes into simulation-ready worlds from a single image or video. Website: research.nvidia.com/labs/gear/simf… Paper: arxiv.org/abs/2606.28276 This work marks a major step for
- Policies trained on real robot data via imitation can be surprisingly capable. But for domains like dexterous manipulation, they are often not quite good enough: they move slowly, miss grasps, make unreliable contact, and fail under small perturbations. Can we improve them
- Very cool use of PolaRiS by @jackyk02 and @XilunZhang1999 as a testbed for their new work in scaling verifiers for robot policies‼️Replying to @jackyk02🧵(6) DROID Eval CoVer-VLA achieves 14% gains in task progress and 9% in success rate on the challenging red-team PolaRiS benchmark. In the pan cleaning task, π₀.₅ shows incorrect intent, grasping the pan handle. In contrast, CoVer-VLA correctly uses sponge to scrub the pan.
