Arhan Jain

@prodarhan

beep boop 🤖 @uwcse

Seattle, WA

arhanjain.github.io

Joined October 2018

385 Following

363 Followers

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Arhan Jain
@prodarhan
Dec 18, 2025
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 🧵)
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Arhan Jain
@prodarhan
Oct 13
@octi_zhang is a legend. the multitask policy learning super impressive
Octi Zhang
@octi_zhang
Oct 13
(1/6) From @GoogleDeepMind’s AlphaGo to AlphaZero, we saw machines find moves that reached beyond what we’d imagined for them. No one taught them those strategies. They emerged from reinforcement learning: playing, failing, and trying again under a single binary task reward for
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Arhan Jain
@prodarhan
Oct 12
sim2real goat
Patrick Yin
@patrickhyin
Oct 9
This robot has never seen a real-life gear, but assembles one with a 94% success rate! It can also thread an M16 nut and insert a 16mm peg. All trained with RL and transferred zero-shot sim2real from RGB. 🔈 Our method, Success-Guided Sampling (SGS), makes a small change to how
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Arhan Jain
@prodarhan
Jul 2
amazing progress on real2sim evals 🤠
Yuke Zhu
@yukez
Jul 2
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
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Arhan Jain
@prodarhan
Jul 2
great work by @yu_raymond5 and @willhuey9 on sim2real & posttraining 😼
Abhishek Gupta
@abhishekunique7
Jul 2
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
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