Bardienus Duisterhof @ IROS 🌉 @bardienus
Incoming Postdoc @stanford || PhD Student @CMU_Robotics with @jeff_ichnowski || Efficient World Models for Robot Manipulation 📷 || bart-ai.com USA Joined October 2018-
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@GeorgiaChal @CSProfKGD @chelseabfinn @vincesitzmann @YunzhuLiYZ We need the 🌶️ takes! @CSProfKGD
The hottest debate of the year: do robots need 🌎 world models 🌎? Join us at CoRL 2026 for a scientific debate on the case for — and against — world models for robotics, featuring @chelseabfinn @vincesitzmann @YunzhuLiYZ @GeorgiaChal! 📄 Paper submissions are open now and close October 12. We welcome both research papers and position papers. 🏆 Submit your work for a chance to win an NVIDIA Jetson Thor or a Samsung Galaxy Tab S11 Ultra! Organized by @longhini_a @wenlong_huang Lasse Peters, @Jefferson_Aero , @PatkiSiddharth @jeff_ichnowski @drfeifei and myself. Thanks to sponsors @NVIDIARobotics and @Samsung!
@CSProfKGD @chelseabfinn @vincesitzmann @YunzhuLiYZ @GeorgiaChal Are you coming to CoRL? 🤩
Excited to speak at this workshop!!
The hottest debate of the year: do robots need 🌎 world models 🌎? Join us at CoRL 2026 for a scientific debate on the case for — and against — world models for robotics, featuring @chelseabfinn @vincesitzmann @YunzhuLiYZ @GeorgiaChal! 📄 Paper submissions are open now and
Wow! Atlas XL go brr brr, congrats team @theworldlabs
World Labs is joining @AMD! Over the past few years our incredible team has pushed the boundaries of spatial intelligence through models like Marble, RTFM, and now Atlas. By partnering with AMD we will train bigger and better models than ever before, and build out an ecosystem
@mateoguaman This is Murray and Woodmont 🐿️
I'll be at IROS this week and looking to discuss anything (efficient) world models for manipulation. Please DM me to set up a coffee chat! ☕️
Today I defended my PhD! 🎓 I came to CMU five years ago to pursue my career, but I’m leaving most grateful for everything I’ve learned about myself along the way. Thank you to everyone who has been part of this journey, including, of course, my committee: @jeff_ichnowski @abhishekunique7 @RamananDeva and @shubhtuls. This also feels like a great time to share what’s next: I’ll be joining @jiajunwu_cs and @GordonWetzstein as a postdoc at Stanford. More soon!
@Hongyu_Lii @YunzhuLiYZ @StefanieTellex @BrownBigAI Congratulations Hongyu!! Excited for your next steps 🚀
Very nice initiative! May I add: - Nutrition does not just improve performance. More importantly, it makes the activity far more enjoyable and you will feel much better after. - Gels can be hit or miss, it is super personal. I tolerate Huma very well and they are very tasty: humagel.com - Don't forget about water and salt!
@Bilibfgg Thanks! The difference is that D4RT is a reconstruction model for getting tracks, depth, and calibration. This would be the training data for PointZero: our model actually predicts the future without observing it, as a generic 3D dynamics model.
Introducing PointZero—a 3D world model pre-trained without robots. Dexterous manipulation requires understanding diverse 3D dynamics, but current approaches rely on expensive robot data. So how can we pre-train a 3D dynamics model without it? PointZero introduces a simple idea: learning to complete 3D point tracks yields transferable 3D dynamics! Our model achieves SOTA results on: 🔥 Zero-shot 3D dynamics 🔥 Action-conditioned 3D dynamics (post-training) 🔥 Imitation learning (post-training) Details and links 👇 1/6
Most world models predict the next video frame. ModAR predicts, not pixels, but motion (point tracks), semantics (DINO), and geometry (depth), predicted one at a time, each one building on the last. Their 30.1M scratch-trained model even outperforms a 6B video-model-initialized model finetuned on the same data.
Can point flow completion serve as a pre-training objective for learning to understand physics? Excited to share PointZero, our recent work on bringing this idea to reality. My favorite part is that the model works zero-shot on real-world images! Take a photo, and then start to simulate it with control points. An embodiment-agnostic, scalable approach. Huge congrats to @bardienus and the team!
Introducing PointZero—a 3D world model pre-trained without robots. Dexterous manipulation requires understanding diverse 3D dynamics, but current approaches rely on expensive robot data. So how can we pre-train a 3D dynamics model without it? PointZero introduces a simple idea:
@mangahomanga Thanks prof @mangahomanga !
@JayKarhade Thank you Mr. 4D!
Instead of training a world model to condition on robot actions, PointZero conditions on partial 3D point tracks. This allows it to learn rich 3D dynamics priors from actionless data! We found that pretraining on this objective effectively transfers to a variety of robotics applications, outperforming strong application-specific baselines on imitation learning and robot-action-conditioned dynamics modeling. We trained PointZero only on synthetic data, but the zero-shot real-world dynamics predictions were surprisingly impressive! Super cool work led by @bardienus
Introducing PointZero—a 3D world model pre-trained without robots. Dexterous manipulation requires understanding diverse 3D dynamics, but current approaches rely on expensive robot data. So how can we pre-train a 3D dynamics model without it? PointZero introduces a simple idea:
Excited to share our new work, PointZero! A 3D world model pretrained on 2.9M synthetic frames with no robot data. By completing 3D point tracks, it learns dynamics that transfer to robot manipulation through fine-tuning. Great collaboration with @bardienus and team!
Introducing PointZero—a 3D world model pre-trained without robots. Dexterous manipulation requires understanding diverse 3D dynamics, but current approaches rely on expensive robot data. So how can we pre-train a 3D dynamics model without it? PointZero introduces a simple idea:
This is a really elegant pre-training paradigm. Go 3D track completion for dynamics understanding!
Introducing PointZero—a 3D world model pre-trained without robots. Dexterous manipulation requires understanding diverse 3D dynamics, but current approaches rely on expensive robot data. So how can we pre-train a 3D dynamics model without it? PointZero introduces a simple idea:
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