PI of the CLVR lab. Associate Professor @KAIST_ai. Prev: assist. prof. @USC, postdoc @Stanford, CS PhD @MIT, BA @UCBerkeley. AI, Robotics, Vision, ML.
Seoul, Republic of Korea
Joined July 2017
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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-b…
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
🧵👇
Joseph Lim retweeted
Very excited to start sharing some of the work we have been doing at Amazon FAR. In this work we present OmniRetarget, which can generate high-quality interaction-preserving data from human motions for learning complex humanoid skills.
High-quality re-targeting really helps the reinforcement learning.
Why? The control policy optimization landscape is much nicer for (near)feasible trajectories than for trajectories with artifacts like (e.g.) foot skating or ground penetration.
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
Joseph Lim retweeted
It's almost time for #CoRL 2025! A reminder that we're hosting the Data in Robotics workshop this Saturday Sept 27th. We have a packed schedule and are also attempting to livestream the event for those who can't attend in person.
Joseph Lim retweeted
We have our speakers: @JosephLim_AI @KarlPertsch @mayacakmak , Serena Booth, @JasonMa2020 @shahdhruv_ @MashaItkina
We also have a great panel lined up from 3-3:40pm!
@svlevine @yukez @MashaItkina @SudeepDasari @ashwinb96 @JasonMa2020 @shahdhruv_
Joseph Lim retweeted
Honored that our @RL_Conference paper won the Outstanding Paper Award on Empirical Reinforcement Learning Research!
📜Mitigating Suboptimality of Deterministic Policy Gradients in Complex Q-Functions
📎openreview.net/forum?id=H3jc…
Grateful to my advisors @JosephLim_AI and @ebiyik_!
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!
🧵
corl.org/home
CoRL 2025 schedule is released now!
Please note that the whole schedule is earlier than other times.
The submission deadline: 4/28
The conference: 9/27-9/30
@corl_conf #corl2025
Do you work on robotics? Do you like robots? Then, visit Korea next year!
We (@ryoo_michael @gupta_abhinav_ @SongShuran Haewon and more) are organizing CoRL 2025 in Seoul, Korea!!
#CoRL @corl_conf #robotics
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.
Joseph Lim retweeted
I've been historically bearish on skill-based RL and @Jesse_Y_Zhang consistently shows me I'm probably wrong. Come check out EXTRACT at 1600 at @corl_conf (poster #11) to see him yank semantically meaningful skills out offline data by leveraging PTLMs.
arxiv.org/abs/2406.17768
Joseph Lim retweeted
This work was guided by Jesse's co-advisor's @ebiyik_ and @JosephLim_AI, but I think we all agree that Jesse is effectively running his own little lab. If you're thinking about hiring a postdoc in the area of RL for robotics, Jesse comes a ton of insight and mentoring experience!
Well.. we won the best system paper award #RSS2023 @RoboticsSciSys !!
@MinhoHeo’s talk can be found at: youtu.be/QXmcu9fVnak?t=7631
We (@MinhoHeo @doohyun22 @YoungwoonLee & I) gave quite a bit of effort into this talk. So, we hope you enjoy the talk and, of course, the project :)
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
🧵👇
My student @MinhoHeo will be giving a talk in 2hrs (11am KST today). #RSS2023
This work is also one of the best system paper finalists too!
Come and check it out :)
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-b…
At #RSS2023 this week w/ some of my (ex-)students from CLVR (@YoungwoonLee @MinhoHeo @doohyun22 @sungj1026 ).
Excited to meet about topics on robot learning, RL, physical reasoning, long-horizon manipulation, etc.
+ Recruiting (int’l & domestic) students. Plz come and say hi!
Then, on Wednesday, we have FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation.
Talk (Wednesday 11am) & Poster (Wednesday 3pm)
By @MinhoHeo @YoungwoonLee @doohyun22
clvrai.github.io/furniture-b…
On Friday, at a workshop, about BOSS — bootstrapping a skill library for unseen environments with LLM
Short Talk (Friday 4:30pm) & Poster(2:45pm)
Room: 306A
By @Jesse_Y_Zhang @JiahuiZhang_32 @KarlPertsch Ziyi Liu @xiangrenNLP @minsuk_chang @shaohua0116
sites.google.com/andrew.cmu.…
Joseph Lim retweeted
Having humans annotate data to pre-train robots is expensive and time-consuming!
Introducing SPRINT:
A pre-training approach using LLMs and offline RL to equip robots w/ many language-annotated skills while minimizing human annotation effort!
URL: clvrai.github.io/sprint/
🧵👇
Joseph Lim retweeted
Excited to present PATO: Policy Assisted TeleOperation, our recent work on scaling robot data collection!
PATO uses a policy trained on prior data to assist the user during data collection, making teleop easier and even allows to teleop multiple robots simultaneously.
🧵👇
Check out our #CoRL2022 paper on learning skill dynamics for model-based RL! We present a sample efficient RL algorithms by temporal abstraction (skills). @YoungwoonLee @lucy_x_shi (an undergrad who will graduate soon!)
