Youngdo Lee

[Robotics: Science and Systems @RoboticsSciSys]

#RSS2026 Awards
🧵1. Outstanding paper
FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control
Donghu Kim, Youngdo Lee, Minho Park, Kinam Kim, Takuma Seno, I. Made Aswin Nahrendra, Sehee Min, Daniel Palenicek, Florian Vogt, Danica Kragic, Jan Peters, Jaegul Choo, Hojoon Lee
https://t.co/fpdIFY8io1

[Dongyoon Hwang @dongyoon_ai]

Introducing "3D HAMSTER", accepted to IROS 2026! 🎉

Hierarchical VLA planners draw waypoints in 2D, but robots act in 3D. Give the VLM a depth encoder, and it predicts metric 3D trajectories robots can execute.
Project page: https://t.co/5TiGDOmHLB
Paper: https://t.co/Rq3rIkUAP4

[Donghu Kim @kty2396]

FlashSAC won the Outstanding Paper Award at RSS 2026 🎉

We got off-policy RL fast and stable enough to beat PPO and FastTD3 across 60+ tasks and 10 simulators with minimal tuning!

TL;DR: If you're working on dexterous manipulation, just try FlashSAC!

https://t.co/hYlhuLRTXn

[Hojoon Lee @hojoon_ai]

We scaled off-policy RL to sim-to-real.
To our knowledge, FlashSAC is the fastest and most performant RL algorithm across IsaacLab, MuJoCo Playground, and many more, all with a single set of hyperparameters.
Project page: https://t.co/uaTcOoYtjt
Paper: https://t.co/PLu6ZGRKuB

[Hojoon Lee @hojoon_ai]

🚀Introducing SimbaV2: A RL architecture that allows compute and parameter scaling via hyperspherical normalization. Built on Soft Actor Critic, using SimbaV2 architecture achieves SOTA results on Mujoco, DMC, Myosuite, HumanoidBench!

Project page: https://t.co/ZCzPiSvm43
⬇️