Robotics Tinkerer. RS @Amazon FAR Prev: @META (FAIR), @DFKI, @TUDarmstadt https://xitter.cf/t.co/RQpq7Prbln X https://xitter.cf/t.co/umZQeDjJv4
Joined November 2017
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Great blog by @ToruO_O !
My view is that we as researchers are becoming into great Rick Rubin
youtu.be/jg1WUOxY6Cg?si=YLdk…
How should we approach research, when the very field we work on is going through rapid and potentially fundamental changes?
I recently gave a talk at the Stanford Vision Seminar, reflecting on my PhD journey and how AI is changing the way I think about research. I adapted part of it into a new blog post, organized around three questions:
1. What should we work on?
2. How should we work with AI?
3. What is a PhD for?
I hope some of my reflections are helpful. The talk ended with an open discussion, and I’d love to continue it here; curious how others are thinking about these questions.
Personally -- as much as there's FOMO, I also think life is very long, and we have time.
toruowo.github.io/blog/posts…
Julen Urain retweeted
A first glimpse of the wildest robotics project of my PhD.
🛹 The Blind Skateboarder 🛹
No vision. No markers.
Paper, code, weights — coming soon. And we have more. 😉
Julen Urain retweeted
Presenting PTLD: an approach to learning tactile dexterous policies without ever simulating the tactile sensor.
Tactile is essential for performing highly dexterous manipulation. However, collecting tactile observations reliably has been a fundamental bottleneck: (1) teleoperating a multi fingered hand for dynamic tasks is challenging, making sim-to-real imperative, (2) one can’t realistically simulate tactile today
Julen Urain retweeted
We are excited to announce the 2026 cohort of RSS Pioneers! This year’s cohort brings together an outstanding group of early-career researchers whose work spans the breadth of robotics. A heartfelt thank you to all the organizers who made this year’s program possible.
Julen Urain retweeted
1/ World models are getting popular in robotics 🤖✨
But there’s a big problem: most are slow and break physical consistency over long horizons.
2/ Today we’re releasing Interactive World Simulator:
An action-conditioned world model that supports stable long-horizon interaction.
3/ Key result:
✅ 10+ minutes of interactive prediction
✅ 15 FPS
✅ on a single RTX 4090🔥
4/ Why this matters: it unlocks two critical robotics applications:
🚀 Scalable data generation for policy training
🧪 Faithful policy evaluation
5/ You can play with our world model NOW at yixuanwang.me/interactive_wo…. NO git clone, NO pip install, NO python. Just click and play!
NOTE ⚠️
ALL videos here are generated purely by our model in pixel space! They are **NOT** from a real camera
More details coming 👇 (1/9)
#Robotics #AI #MachineLearning #WorldModels #RobotLearning #ImitationLearning
Julen Urain retweeted
Pretraining with dynamics models of motor behavior (aka world models) from video will be much more central to robotics than VLMs. There are multiple choices of representations (e.g. 3D? JEPA?) but we will figure this out by and by. Exciting times!
- Project website: dreamdojo-world.github.io/
- Paper: arxiv.org/abs/2602.06949
- Code repo and model ckpts: github.com/NVIDIA/DreamDojo
This is a huge team work at NVIDIA. All credits go to the wonderful teams who poured their hearts into it!
Julen Urain retweeted
Fully agreed with the sentiment that much of computer vision research (concretely, those not for “human consumption”) should be grounded in robotics. But as a robotics researcher, I think the more nuanced question is: how can we *rethink* these intermediate representations for embodied intelligence rather than discarding them?
Why?
The challenge, as also pointed out in Vincent’s article, is precisely the lack of perception-action data at scale. This is why intermediate representations IMO are *preferable rather than obsolete* because they open up training from scalable data sources. This can include even the vision/language encoders people love and use in robot learning — it’s hard to imagine training low-level visual representation or high-level language understanding purely from limited robot data. The same goes for intermediate representations at the structure level — world modeling, learning from Internet videos, learning from humans, and simulation — many of which still rely on 3D representations too.
In my recent blog post, I argue that "vision" is only well-defined as part of perception-action loops, and that the conventional view of computer vision - mapping imagery to intermediate representations (3D, flow, segmentation...) is about to go away.
vincentsitzmann.com/blog/bit…
Julen Urain retweeted
At the RI seminar at CMU yesterday, I presented a 3 level analysis of robot skills & discussed the pros and cons of teleoperation, simulation, and learning from videos, before presenting our research. Enjoy! youtube.com/watch?v=ry8itipz…
It reminds me to TD learning or even GAIL.
I am not convinced of bootsrapping for generative models .
Julen Urain retweeted
As video world models become increasingly powerful, do we still need explicit 3D?
A commonly misunderstood point is this: video world models are not “just 2D.” Their ability to maintain multi-view consistency, temporal stability, and realistic interaction necessarily implies that their latent knowledge encodes 3D world structure. Without some notion of 3D, consistency itself would not be possible.
The real distinction, therefore, is not whether a model has 3D but whether that 3D exists implicitly or explicitly.
Implicit 3D lives inside latent spaces and network weights. It supports generation, but it is difficult to localize, edit, constrain, or reason about.
It allows the world to exist, but not to be used.
Explicit 3D, in contrast, exists as structure and state: it is addressable, editable, composable, and transferable.
Its purpose is not better visual fidelity, but operability to allow the world to be manipulated, controlled, and executed.
From this perspective, video and 3D are not competing paradigms but a layered system:
2D/video is the interface to human perception; 3D is the interface to the physical world.
They can reinforce each other, but neither forms a closed loop on its own.
In practice, data not model architecture sets the upper bound of world models.
Explicit 3D may not be the final user-facing representation, but it is likely the most effective pathway toward scalable, high-quality, and controllable data.
Through explicit 3D/4D representations, worlds can be constructed systematically:
interactions can be programmatically sampled, states and actions can be composed, rendered into images and videos, and fed back to train video world models.
Seen this way, 3D is not the destination it is the starting point for scaling.
What truly drives progress forward is never the model itself.
Whether we capture the world or imagine new ones,
whether data comes from observation or intent,
whether we model what is or what should be the direction of the world is ultimately determined by human choice and purpose.
Models may extend the world,
but humans decide where it goes.
#Genie3 #worldmodel
There has been a clear trend in the last months moving from VLA-type approaches to Video Generative Models + Inverse Dynamics Models (VAM).
While the probable main reason of this recent growth is the latest improvements in video generative models, I believe this shift is relevant for robotics.
While the VLA's distill the foundation models knowledge through some latent representations that intertwine semantic and spatial information, VAM distill this knowledge in a more explicit way, representing it spatially.
I believe this spatial grounding of VAM might lead to way larger generalization capabilities wrt. VLA and I am optimistic in even more 3D spatially grounded foundation models in the direction of the recent @wenlong_huang point-world.github.io/
Very cool! Happy to see the Value-function based approach works that well! Very refreshing approach in front of end-2-end generative model approaches :)