🔥#Legged_Robot_SLAM, State Estimation & Locomotion🔥 KAIST Urban Robotics Lab Ph.D
Joined December 2016
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During my graduate course, I had the opportunity to integrate a Mesh network-based Distributed Multi-Robot communication and evaluation system. This system utilized a synchronized time handler across robots to obtain reliable evaluation metrics. This system was also utilized in our latest work, Commerge.
youtu.be/LV_8_jZbHV8?si=PzIT…
![[IJRR 2026] Commerge: Communication-Efficient, Robust, and Fast LiDAR Map Merging Framework](https://pbs.twimg.com/card_img/2113904645861228544/-P8i63cz?format=jpg&name=800x419)
Jason Kim retweeted
Happy to share our SSIO! Very exciting direction to train IMU without labels!
🤖 𝐒𝐒𝐈𝐎: 𝐒𝐞𝐥𝐟-𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐈𝐧𝐞𝐫𝐭𝐢𝐚𝐥 𝐎𝐝𝐨𝐦𝐞𝐭𝐫𝐲
Since the first learned inertial odometry 10 years ago, every method has needed ground-truth labels.
It doesn't have to: SSIO learns from raw IMU, fully self-supervised 🧵👇
ssio-imu.github.io
Jason Kim retweeted
Vision foundation models like DINO and SigLip now power much of robot learning.
But they are built for RGB. What about depth?
Introducing DeFM: a foundation model for depth images, built for robotics.
🌐 de-fm.github.io
Weights, code, and demo are out today 🧵
Jason Kim retweeted
🤪maintainer of GTSAM, Gatech phd, auto rejected for a state estimation role.
Hiring in robotics is so funny. I applied to @shieldaitech for a state estimation role, especially since the job description listed being familiar with libraries like GTSAM. You'd think I would be a great fit.
Got a rejection email in less than a week without any interviews. :)
Jason Kim retweeted
This was a fun collaboration with an amazing team 🙌: @breadli428, Amin Abyaneh, @aahoussaini, Kirsty Ellis, @GlenBerseth, Marco Hutter, and Hsiu-Chin Lin from McGill, Mila, ETH Zürich & UdeM.
@mcgillu @Mila_Quebec @ETH_AI_Center @leggedrobotics @UMontreal
📄 arXiv:
arxiv.org/abs/2604.08780
🌐 Project page (w/ videos):
modanesh.github.io/papers/qw…
Jason Kim retweeted
An early video of testing state estimation solutions for box climbing. Livox+dlio vs odin1
With @JayHe748646 @KehanWen170077 @leggedrobotics , we push humanoid mobility to next frontier.
Achieved by a generalist contact planner + a generalist tracker + a generalized neural mapping, warmstarted from few human data.
Website: leggedrobotics.github.io/ACP…
Promo Video 👇
Jason Kim retweeted
How can we represent whole-body interaction for humanoid robots—compactly enough for low-frequency planning, yet richly enough to preserve high-frequency feedback control?
Introducing FIBER! 🤖
FIBER learns a unified latent interface between high-level planning and whole-body control, enabling a humanoid to acquire diverse interaction behaviors from heterogeneous experts through a pretrained, frozen Behavior Foundation Model (BFM).
With a single planner and BFM, we demonstrate heavy-door pushing, box carrying, object grasping, controllable navigation, and more!
🌍 Website & Live Demo: fiberaa.github.io
📄 Paper: arxiv.org/abs/2610.24675
A thread on the ideas behind FIBER 🧵👇
Jason Kim retweeted
No prior data. No rewards. No pretrained models.
Can an RL agent learn to solve extremely long-horizon tasks (e.g., 5-cube stacking) from scratch?
We show it can! C-Slerp learns hyperspherical contrastive representations and infers waypoints from them in closed form 🧵👇
Jason Kim retweeted
📢OneCanvas (NeurIPS'26 Spotlight) Code Release📢
What if a 2D VLM could see the whole scene in one view?
We reproject features from every frame into a single occlusion-free canvas to make a stock VLM reason in 3D!
🔗 github.com/baranowskibrt/one…
🌐 baranowskibrt.github.io/onec…
![GitHub - baranowskibrt/onecanvas: [NeurIPS 2026 Spotlight] Official code and checkpoints for...](https://pbs.twimg.com/card_img/2113639250210017280/ef1EFTti?format=jpg&name=800x419)
📢 OneCanvas: 3D Scene Understanding via Panoramic Reprojection
We extract features from video frames and reproject them into one occlusion-free view of the whole scene that a 2D VLM reads just like a normal image. We can center this view on any viewpoint, including an agent's own pose for situated reasoning.
The same projection lets us create spatial training tasks with no human annotation, solvable only by reasoning over the 3D positions of real object features placed on an otherwise empty canvas.
The result is a stock 2D VLM that reasons in 3D, setting a new state of the art across spatial benchmarks at far less compute.
🌐 baranowskibrt.github.io/onec…
▶️ youtu.be/NIaHLB9gA7s
Great work by @baranowskibrt & @davech2y
Jason Kim retweeted
We’ll have two presentations at #IROS2026, featuring EFGCL and its extension, WARL!
We’d love to see you there!
Details about the presentations are in the thread below ↓
Jason Kim retweeted
⚡ One policy, millions of embodiments, over 200 robot models. Can we add yours?
We're building γ₀, a generalist RL policy for motion control trained across millions of randomized embodiments derived from a growing collection of more than 200 robot models.
Jason Kim retweeted
Our LT-Mem paper won the Best Paper Award at IROS 2026 — 1 out of 1,933 papers!
Huge congratulations to Yumin Lee and Hyoseok Ju. Proud of our APRL team at DGIST!
lt-mem.github.io/
#IROS2026 #Robotics #SpatialIntelligence #DGIST #APRL
