Robotics & Computer Vision Professor at Georgia Tech, and part-time CAIO at Verdant Robotics. Before: stints at KUL, Skydio, Facebook B*8, Google AI.
San Mateo, CA
Joined June 2008
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My Annual Reviews article on Factor Graphs in Robotics is finally out with a publicly accessible link: annualreviews.org/eprint/85Pโฆ
Frank Dellaert retweeted
Check out ๐ ๐ผ๐ฟ๐ฝ๐ต๐ ๐ถ๐บ๐ถ๐ฐ, a ๐ฐ๐ฟ๐ผ๐๐-๐ฒ๐บ๐ฏ๐ผ๐ฑ๐ถ๐บ๐ฒ๐ป๐ ๐๐ผ๐ฟ๐น๐ฑ ๐บ๐ผ๐ฑ๐ฒ๐น and ๐๐ต๐ผ๐น๐ฒ-๐ฏ๐ผ๐ฑ๐ ๐บ๐ผ๐๐ถ๐ผ๐ป ๐๐ฟ๐ฎ๐ฐ๐ธ๐ฒ๐ฟ for Unitree G1, AGIBOT X2 Ultra, and Booster T2 humanoid robots: lzyang2000.github.io/morphmiโฆ
One world, one policy.
MorphMimic shares a morphology-conditioned world model and policy across #Unitree G1, #AgiBot X2 Ultra, and #Booster T2โfrom the same dance in MuJoCo to a live browser demo and Unitree G1 hardware, with more to come. Code and report at lzyang2000.github.io/morphmiโฆ
Huge thanks to the SuperMimic team for the inspiration!
Frank Dellaert retweeted
FoundationVIO: Enforcing Metric Scale in Feed-forward Visual-Inertial Odometry
Jin Rhee, Yuhao Zhang, @fdellaert, @MauriceFallon
tl;dr: IMU measurements + GFM predictions->factor graph
arxiv.org/abs/2610.14951
Did I say GTSAM 4.3 is available? If using @gtsam4 I recommend switching to the latest version. We'll keep develop on a 4.3 version for now to quickly react to any breaking changes - but really only expect better performance :-) gtsam.org/
Frank Dellaert retweeted
Meet prototype 1 of the Goose, the family robot.
We're building for the home: tidy, play, live.
Frank Dellaert retweeted
๐๐๐๐ช
โข #1 Public Best Value College Overall: Georgia Tech
โข #1 Private Best Value College Overall: Princeton
princetonreview.com/press/beโฆ
Frank Dellaert retweeted
๐ Excited to share our recent work on deformable object manipulation, ๐ฃ๐ต๐๐๐๐ผ๐ฅ๐ฒ, accepted to ๐๐ผ๐ฅ๐ ๐ฎ๐ฌ๐ฎ๐ฒ. Congrats to @hcy1n @ShuohanT!
๐๐ผ๐ฑ๐ฒ & ๐๐ฎ๐๐ฎ: lunarlab-gatech.github.io/Phโฆ
#CoRL2026 #RobotLearning #DeformableObjects
Excited to share our #CoRL2026 work PhysCoRe: a physics-corrected world model for deformable objects!
We couple a physics-based simulator with two feed-forward modules: one infers material from vision, the other corrects its internal dynamics.
Check: lunarlab-gatech.github.io/Phโฆ
So proud of my daughter and her PlosOne publication on gene expression in corals, when they are under thermal stress.
journals.plos.org/plosone/arโฆ
You fine-tune a robot foundation model on a hard task. It gets 25%. Now what?
Introducing Q-Planning, a learning-based harness that lets large black-box robot policies recursively self-improve.
On a hard fine-grained task: 25% โ 80% in 100 robot attempts (~30 mins), no extra human data.
q-planning.github.io/
1/n ๐งต
GTSAM is going GPU! ๐
Ruogu Li has built an impressive new experimental CUDA backend for nonlinear factor-graph optimization in GTSAM, just in time for the 4.3 release next week.
Read our blog post:
gtsam.org/2026/08/20/cuda-baโฆ
#GTSAM #CUDA #Robotics #SLAM #StructureFromMotion #BundleAdjustment #ComputerVision #GPU #OpenSource
๐ค Made with AI
Follow up PR also made SFM much easier to use, see borglab.github.io/gtsam/sfm/
Exciting to see how the community puts @gtsam4 to work! In a guest post, Jash Shah explores an application of factor graphs to 3D Gaussian Splatting, bringing neural rendering together with pose graphs, loop closures, robust noise models, and iSAM2.
gtsam.org/2026/08/04/gaussiaโฆ
#GTSAM #SLAM #3DGS
