Tal Daniel

Postdoc @ CMU Robotics Institute
Ph.D. from the Technion ECE
Research interests include self-supervsied learning, generative modeling, RL, robotics.

[Tal Daniel @TalDaniel8]

🚀 #ICLR2026 Oral 💥
How can we design world models that capture object interactions directly from pixels?
Introducing Latent Particle World Models-the first end-to-end self-supervised, object-centric world model, trained from videos, supporting action/img/lang conditioning.
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[Kallol Saha ✈️ CoRL '26 @_ksaha]

🚨Introducing DISCO🪩: Distilled Tree Search over Continuous Actions (Best Paper Finalist🏆 at #CoRL2026)

DISCO combines learned policies with Monte-Carlo Tree Search (MCTS) over continuous actions for long-horizon manipulation tasks.

A thread 🧵👇

[Yishu Li ✈️ CoRL 2026 @LisaYishu]

Robots can’t always see the physics they act under: hidden mass, friction, kinematic constraints. A scalar reward only says "failed." The outcome of an action says much more.

Introducing SCOUT: test-time policy adaptation from action-outcome feedback. Sim-trained, adapts in the real world in a few tries 🧵👇 #CORL2026

[Raphael Pisoni @ml_4rtemi5]

What if I told you that Scaled Dot-Product Attention (SDPA) doesn't make a whole lot of sense for ViTs and you can get faster convergence, better performance and near-linear scaling with a few well-steered Gaussians?
Let me introduce you to SSOG (Separable Sum Of Gaussians)!

[Saket Tiwari @SaketTiwari14]

@aimalysheva @mmmbchang Kinda relevant to this kind of an object-centric approach: https://t.co/zyrmJV7MsS (ICLR 2026)

[Sriram Krishna ✈️ CoRL '26 @sriramsk99]

Boo! 👻 GHOST has been accepted to #RSS2026!

Can we learn manipulation skills from human video (without action retargeting)?

Yes! GHOST learns generalizable manipulation skills by training a hierarchical policy with an embodiment-agnostic sub-goal predictor + embodiment-specific controller.

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[Tal Daniel @TalDaniel8]

Self-supervised object-centric learning goes 3D! Get KP, BB and segmentations directly in 3D with 0 annotaions, super useful for robotic manipulation! Amazing work by @EllinaZhang (she will be at #ICML2026 in-person)!

[Ellina Zhang @EllinaZhang]

🧩 #ICML2026 💥 How can a model discover the 3D objects in a scene—their shape, color, and position—without any labels? Introducing 3D-DLP, a self-supervised object-centric model that decomposes colored 3D scenes (RGB-D and voxels) into a set of 3D latent particles.

[Aviv Tamar @AvivTamar1]

Excited to announce that our new book Reinforcement Learning Foundations is available for preorder!
W/ @shiemannor and @YishayMansour

30% discount and free shipping till July 21
Discount code 148294
https://t.co/KH7RfAkC4g

[Tal Daniel @TalDaniel8]

Object-centric representations for tactile data! And for a good cause! Great work!

[Zohar Rimon 🛩️ CoRL @ZoharRimon]

Excited to present the first-ever 3D, generalizable, handheld tactile imaging system! 👇

Our system learns to visualize the inner structure of soft objects purely through touch -
"More with LESS: Local Scene Representation for Tactile Imaging," has been accepted to #RSS2026 🇦🇺

Curious how we leverage the locality of touch for large-scale tactile SSL? Read on! 🧵

[Aviv Tamar @AvivTamar1]

VLA is 95% certain about current action. Will it 95% succeed in the task?

Obviously, not necessarily. But if you’re clever, you can *calibrate* action prob. to task success.

Our #ICML2026 paper formulates this + SOTA algorithms based on new connection to RL temporal differences

[Tal Daniel @TalDaniel8]

@AvivTamar1 ❤️

[Tal Daniel @TalDaniel8]

I’m attending #ICLR2026 in Rio this week to present LPWM! Friday April 24 Poster Session 3 10:30, Oral Session 4B 3:15p. Happy to chat about self-sup object-centric learning and world models. I’ll be on the job market soon and looking for exciting opportunities!

#ICLR @iclr_conf

[Tal Daniel @TalDaniel8]

🚀 #ICLR2026 Oral 💥
How can we design world models that capture object interactions directly from pixels?
Introducing Latent Particle World Models-the first end-to-end self-supervised, object-centric world model, trained from videos, supporting action/img/lang conditioning.
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[Dan Haramati @DanHrmti]

I will be at #ICLR2026 this week to present our work on Hierarchical Entity-centric Reinforcement Learning!

Come by our poster (Thursday Poster Session 2 P4-#4712) and reach out anytime to talk about #ReinforcementLearning #WorldModels #HierarchicalRL

[ML@CMU @mlcmublog]

Check out our latest post on CMU@ICLR 2026!

https://t.co/TzSd2zs4sT

[Sandeep Routray @SandeepRoutra11]

🚀 Excited to share ViPRA: Video Prediction for Robot Actions

📍 Accepted to #ICLR2026 @iclr_conf
🏆 Best Paper — #NeurIPS2025 Embodied World Models Workshop

Robot learning today still needs millions of action labeled videos.

Yet videos are abundant — from humans and the web — but lack action labels. Meanwhile, pretrained video models already learn rich dynamics.

ViPRA is a recipe for turning pretrained video models into robot policies while enabling robot learning to scale with actionless videos.
🧵 Thread ↓

[Tal Daniel @TalDaniel8]

Join us for live paper (Latent Particle World Models) overview + Q&A today (March 24) at 4pm PT / 7pm ET!
https://t.co/KRgXI2RGWe

[Cecile Tamura @ceciletamura]

🎤 Dr. @TalDaniel8 (@CarnegieMellon ) In a deep dive
w/ @ceciletamura of @ploutosai

How can AI discover objects, model uncertainty, and predict the future from raw video alone?

🔴 [https://t.co/yimiKyJeYV](https://t.co/yimiKyJeYV)

[Tal Daniel @TalDaniel8]

@DanHrmti @carl_qi98 @LambdaAPI @AvivTamar1 @davheld @deepakpathak 🌐 Learn more about Latent Particle World Models (LPWM):
Website: https://t.co/nqDE1UyviV
Paper: https://t.co/cMFrfsN00p
Code (+ tutorial): https://t.co/0kJskfdm8O
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[Tal Daniel @TalDaniel8]

During training, the posterior latent actions condition the dynamics module that predicts the next-frame prior.
A KL regularization term aligns this prediction with the latent policy’s output, forming a VAE-style objective over particle transitions.
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[Tal Daniel @TalDaniel8]

Huge thanks to @DanHrmti, @carl_qi98, Amir Zadeh and Chuan Li (@LambdaAPI), @AvivTamar1, @davheld, and @deepakpathak — incredible collaborators who made LPWM possible.
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