Bernhard Jaeger

GitHub - autonomousvision/CaRL: [CoRL 2025] CaRL: Learning Scalable Planning Policies with Simple Rewards [CoRL 2025] CaRL: Learning Scalable Planning Policies with Simple Rewards - autonomousvision/CaRL

Our paper "CaRL: Learning Scalable Planning Policies with Simple Rewards" has been accepted to the Conference on Robot Learning (CoRL 2025).
See you in Seoul at the end of September.

Code & Paper:
github.com/autonomousvi...

My lab will be recruiting at all levels. PhD students, postdocs, and a research engineering position (worldwide for PhD/postdoc, EU candidates only for the engineering position). If you're at SIGGRAPH, I'd love to talk to you if you are interested in any of these.

You are right, terrorists (and other illegal organizations) do not care about licenses.
But these organizations typically do not make weapons themselves; they buy or smuggle them.

That is the reason why we restrict building weapons. This happens in corporations that are subject to legal oversight.

If they work at a large company, then their legal team will.

But more importanty good actors do read licenses!
We kept it super concise to respect their time.

valid concern. We discuss this question in the Q&A civil-software-licenses.github.io/qa.html

One concern that I have as an AI researcher when publishing code is that it can potentially be used in dual-use applications.
To solve this, we propose Civil Software Licenses. They prevent dual-use while being minimal in the restrictions they impose:

civil-software-licenses.github.io

C++ questions you can get answers from LLMs.
For repo-specific questions, you can use GitHub issues.

Singularity has become the new standard in large GPU-clusters (particularly in academia) where Dockers root access leads to security problems.

it is planned to be stable. 3.14 isn't out yet. They need to get it stable, get the overhead down and then all the necessary libraries need to add support. I am hopeful that in 1-3 years you can do this in Python as well.

Our code release for RL for driving comes with a full multi-GPU implementation of PPO in C++ using Libtorch.
C++ allows you to implement fully asynchronous data collection using multi-threading and CUDA streams.
This is not possible in Python due to the GIL problem.

github.com/autonomousvi...

We are excited that Scholar Inbox is supporting ACL 2025
@aclmeeting.bsky.social with personalized conference programs this year for the first time!
www.scholar-inbox.com/conference/a...
It would be great if you could widely share this news within the NLP community. See you next week in Vienna!

Our Machine Learning for Science Cluster in Tübingen @ml4science.bsky.social is extending its collaboration with the African Institute for Mathematical Sciences #AIMS: We hire a joint research group leader, based in Kigali. Application deadline: August 31.

tinyurl.com/38z4e6dy

Seems like combining on-policy Q-Learning (critic) gradients with policy gradients is the way to get both good sample efficiency and high performance.

Overview figure 1 paper from "Robust Autonomy Emerges from Self Play"

You've got a couple of GPUs and a desire to build a self-driving car from scratch. How can you turn those GPUs and self-play training into an incredible looking agent? Come to W-821 at 4:30 in B2-B3 to learn

scholar inbox is funded by a grant from the volkswagen foundation currently.

Okay this deserves a quote post this is a game-changer for attending conferences

Back in 2023, I worked on rule-based planning for the nuPlan challenge and was skeptical that learned methods could do well on this benchmark. Turns out, RL is the answer. The CaRL code is out now. Also check out this 2h+ demo video of our policy: youtu.be/_godUKkICec

GitHub - autonomousvision/CaRL: [ArXiv 2025] CaRL: Learning Scalable Planning Policies with Simple Rewards [ArXiv 2025] CaRL: Learning Scalable Planning Policies with Simple Rewards - autonomousvision/CaRL

We have released the code for our work, CaRL: Learning Scalable Planning Policies with Simple Rewards.

The repository contains the first public code base for training RL agents with the CARLA leaderboard 2.0 and nuPlan.

github.com/autonomousvi...

🏟️But we’re not done yet - our workshop continues at #ICCV2025! And the challenge moves forward too, with more prizes and exciting updates. opendrivelab.com/challenge2025/

📹Our #CVPR2025 workshop and tutorial recordings are now online! Big thanks to our incredible speakers! Watch all the sessions here
🔗 Workshop: youtube.com/playlist?lis...
🔗 Tutorial: youtube.com/playlist?lis...

During the last couple of years, we have read a lot of papers on explainability and often felt that something was fundamentally missing🤔

This led us to write a position paper (accepted at #ICML2025) that attempts to identify the problem and to propose a solution.

arxiv.org/abs/2402.02870
👇🧵

Hm, what is the point of using SAC if you first have to run PPO to figure out the action boundaries? This seems like a suboptimal solution.

I guess that is the case for company funding?
The foundations I have interacted with seemed to like risk, and there is not much of a deliverable other than a report at the end of the project. It might also be different from foundation to foundation, they can mostly run their grants how they want.

The only way I can navigate the ML literature these days is #ScholarInbox. At conferences, it builds a list of relevant posters for each session and I don’t know what I’d do without it. I 100% recommend this tool.

www.scholar-inbox.com

not just that they also impose much less reporting and control restrictions once you have the money

🚗 Excited to announce our RealADSim Workshop & Challenges @ ICCV 2025 — join us!
🏆 Over $40,000 in prizes
🌆 Competition 1: Extrapolated Novel View Synthesis
🚘 Competition 2: Closed-loop Driving in a 3DGS-based Simulator
🎤 Featuring great speakers
🔗 realadsim.github.io/2025/

Scholar Inbox: Daily Research Recommendations just for You Science is moving fast. How can we keep up? Scholar Inbox helps researchers stay ahead by making the discovery of open access papers more personal.

On our blog: Science is moving fast. How do we keep up? #ScholarInbox, developed by the Autonomous Vision Group led by @andreasgeiger.bsky.social, helps researchers stay ahead - by making the discovery of #openaccess papers smarter and more personal: www.machinelearningforscience.de/en/scholar-i...

@bsky.app new feature request: a mute button for American politics. 😅🙏

The site is awesome but when stuff happens in the US we hear about it for weeks and there's no escape 😭

Christina's new work PrITTI generates high-quality, controllable 3D semantic urban scenes in a compact primitive-based representation using a latent diffusion model. This enables applications like scene editing, inpainting, and photo-realistic street view synthesis!
raniatze.github.io/pritti/

Very happy that finally - after ~2 years of review - PanopticNERF-360 has been accepted to TPAMI :)
Panoramic 3D-to-2D Label Transfer in Urban Scenes. Kudos to the team and Yiyi Liao for leading the effort. Here is the project page: fuxiao0719.github.io/projects/pan...

Robust Autonomy Emerges from Self-Play Self-play has powered breakthroughs in two-player and multi-player games. Here we show that self-play is a surprisingly effective strategy in another domain. We show that robust and naturalistic drivi...

Hiring a postdoc to scale up and deploy RL-based planning onto some self-driving cars! We'll be building on arxiv.org/abs/2502.03349 and learn what the limits and challenges of RL planning are. Shoot me a message if interested and help spread the word please!

Full posting to come in a bit.