Joseph Lim

Looking for a challenging manipulation benchmark? Introducing FurnitureBench 🪑🛠️, a reproducible real-world furniture assembly benchmark (#RSS2023 @RoboticsSciSys)! It features * 8 furniture models * 200+ hours of expert demonstrations * FurnitureSim: Isaac Gym simulator 🧵👇

Humanoid motion tracking performance is greatly determined by retargeting quality! Introducing 𝗢𝗺𝗻𝗶𝗥𝗲𝘁𝗮𝗿𝗴𝗲𝘁🎯, generating high-quality interaction-preserving data from human motions for learning complex humanoid skills with 𝗺𝗶𝗻𝗶𝗺𝗮𝗹 RL: - 5 rewards, - 4 DR terms, - Proprio. ONLY, - NO history/curriculum. Ready for agile, human-like 🤖? (Best with 🎧) 🔗 omniretarget.github.io 🎥 1/9

At @RL_Conference🍁, I'm presenting a talk and a poster on Aug 6, Track 1: Reinforcement Learning Algorithms. We find that Deterministic Policy Gradient methods like TD3 often get stuck at local optima under complex Q-functions, and propose a novel actor architecture! 🧵

I am extremely pleased to announce that CoRL 2025 will be in Seoul, Korea! The organizing team includes myself and @gupta_abhinav_ as general chairs, and @JosephLim_AI, @songshuran, and Hae-Won Park (KAIST) as program chairs.

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Looking for a challenging manipulation benchmark? Introducing FurnitureBench 🪑🛠️, a reproducible real-world furniture assembly benchmark (#RSS2023 @RoboticsSciSys)! It features * 8 furniture models * 200+ hours of expert demonstrations * FurnitureSim: Isaac Gym simulator 🧵👇

We are finally releasing a new benchmark!! #RSS2023 Main highlights are: * For real-world * Reproducible (3D printing) * 9 furniture assembly long-horizon tasks * 200+ hrs of demonstrations This apparently is my 3rd furniture-related benchmark.🥹 clvrai.github.io/furniture-bench