Doohyun Lee

PhD student in KAIST AI

[Kaixin Chai @Kaixin_Chai]

🚀 Introducing N2M!

In mobile manipulation, the performance of a manipulation policy is very sensitive to the robot’s initial pose. N2M guides the robot to a suitable pose for executing the manipulation policy.

N2M comes with 5 key features - Check them out in the posts below!

[Minho Heo @MinhoHeo]

Come see what robot learning can do for surgical automation!
We’re excited to host the first Workshop on Automating Robotic Surgery with an amazing lineup of speakers.

🗓️ Sept. 27 09:30AM - 12:30PM
📍 Floor 3F, Room E7
🌐 https://t.co/Lp10McSvb2

#CoRL2025 #CoRL @corl_conf

[Carlo Sferrazza @carlo_sferrazza]

Humanoids 🤖 will do anything humans can do. But are state-of-the-art algorithms up to the challenge?

Introducing HumanoidBench, the first-of-its-kind simulated humanoid benchmark with 27 distinct whole-body tasks requiring intricate long-horizon planning and coordination.

🧵👇

[Karl Pertsch @KarlPertsch]

Access to *diverse* training data is a major bottleneck in robot learning. We're releasing DROID, a large-scale in-the-wild manipulation dataset. 76k trajectories, 500+ scenes, multi-view stereo, language annotations etc
Check it out & download today!

💻: https://t.co/JsbBZIxzZA

[Minho Heo @MinhoHeo]

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

🧵👇

[Jesse Zhang @Jesse_Y_Zhang]

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: https://t.co/KuxuUeaXmA

🧵👇