Ignat Georgiev

Robot Learning PhD @ Georgia Tech

[Ignat Georgiev @imgeorgiev]

We have a new ICML paper! Adaptive Horizon Actor Critic (AHAC). Joint work with @krishpopdesu @xujie7979 @eric_heiden @animesh_garg

AHAC is a first-order model-based RL algorithm that learns high-dimensional tasks in minutes and outperforms PPO by 40%.
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[Danfei Xu @danfei_xu]

Introducing EgoVerse: an ecosystem for robot learning from egocentric human data.

Built and tested by 4 research labs + 3 industry partners, EgoVerse enables both science and scaling

1300+ hrs, 240 scenes, 2000+ tasks, and growing

Dataset design, findings, and ecosystem 🧵

[Albert Wilcox @albertwilcoxiii]

Heading to Seoul to present this work at CoRL 2025! Looking forward to lots of fun discussions this weekend. If you want to chat sometime please send a DM!

[Albert Wilcox @albertwilcoxiii]

Imitation learning has seen great success, but IL policies still struggle with OOD observations

We designed a 3D backbone, Adapt3R, that can combine with your favorite IL algorithm to enable zero-shot generalization to unseen embodiments and camera viewpoints!

[Jeremy Collins @jerthesquare_]

Robotics data is expensive and slow to collect.

Robotics labs and companies spend months just to collect around 10k hours of demonstration data, all while that much video is uploaded to YouTube every 20 minutes. However, none of this video data contains action labels. How can we bridge the gap?

AMPLIFY solves this problem by learning Actionless Motion Priors that unlock better sample efficiency, generalization, and scaling for robot learning.

[Ignat Georgiev @imgeorgiev]

Exciting opportunity!

[Edward Johns @Ed__Johns]

I have Post-Doc, PhD, and Research Assistant positions available, for a new project working with me on dexterous robot learning!

Come and join me, my team, and our robots, at Imperial College London! 🤖🧑‍💻🇬🇧💂🤖

See https://t.co/MdnQJcLd9p for more info.

Please share/retweet!

[Ignat Georgiev @imgeorgiev]

@Stone_Tao Yep fully agree with all you mentioned. I'm not sure if better rewards or task decomposition is the answer though. Here's one more piece of thought. Rewards we can engineer to be smoother but even then how do we make the dynamics smooth?

[Ignat Georgiev @imgeorgiev]

Behavior Cloning (BC) has been the new hot thing in #Robotics for the past year. I finally sucked my teeth into it and tried to decipher why it has worked so well for problems where RL struggles

https://t.co/tb8vnUH7sI

Let me know if you have other interesting perspectives!

[Ignat Georgiev @imgeorgiev]

@adv8p Yeah I had this point brought up on linkedin. Dagger is very much still a theoretical issue but for some reason I don't see it materialize that often now in the world of large(r) data and trajectory chunking. Thoughts?

[Utkarsh Mishra @utkarshm0410]

How can robots compositionally generalize over multi-object multi-robot tasks for long-horizon planning?

At #CoRL2024, we introduce Generative Factor Chaining (GFC), a diffusion-based approach that composes spatial-temporal factors into long-horizon skill plans. (1/7)

[Ignat Georgiev @imgeorgiev]

@jia_xuhui Just published a paper on multi-task deep RL with large models, so I'm very interested in this opportunity!

[Ignat Georgiev @imgeorgiev]

Completely forgot to give the time - Hall C 4-9, Wednesday, 7:30 - 9:00 am!

[Ignat Georgiev @imgeorgiev]

We derive a bound on this sample error and find that backproping through contact. From these insights, we propose AHAC, which dynamically adapts its horizon to avoid differentiating through contact. AHAC scales to 152 action dimension tasks and beats model-free baselines by 40%!

[Ignat Georgiev @imgeorgiev]

You can find the paper, video and code on our website https://t.co/BonnnTEpWc

[Ignat Georgiev @imgeorgiev]

AHAC is a first-order RL method that uses gradients from the sim to learn fast and also better policies - outperforming PPO by 40%. Differentiable simulations are a powerful framework to scale RL. However, even when given ground-truth dynamics, not all gradients are useful!

[Ignat Georgiev @imgeorgiev]

Excited for ICML next week! I'll be presenting Adaptive Horizon Actor Critic - a model-based RL method that learns high dim tasks in minutes using differentiable simulation.

Stop by Hall C 4-9 or get in touch if you want to grab a coffee some other time!

More on AHAC in the 🧵

[Ignat Georgiev @imgeorgiev]

This means that PWM can scale to billion-parameter models more effectively. Incredibly, multi-task PWM almost matches the performance of single-task experts like DreamerV3 and SAC

Check out the paper, code and models at https://t.co/p7dL83JUvr

[Ignat Georgiev @imgeorgiev]

🔔New Paper - PWM: Policy Learning with Large World Models

Joint work with @VarunGiridhar3 @ncklashansen @animesh_garg

PWM is a multi-task RL method which solves 80 tasks across different embodiments in <10m per task using world models and first-order gradient optimization🧵

[Ignat Georgiev @imgeorgiev]

We also tested PWM on 30 and 80 multi-task settings from dm_control and MetaWorld. After training a single large multi-task world model, we extract policies in <10 min per task using PWM. We surpassed TD-MPC2 by 27% and 8% respectively, without the need for online planning! 🧵