Postdoc @ CMU Robotics Institute Ph.D. from the Technion ECE Research interests include self-supervsied learning, generative modeling, RL, robotics.
Joined November 2019
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Tal Daniel retweeted
🚨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 🧵👇
Tal Daniel retweeted
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
Tal Daniel retweeted
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)!
Tal Daniel retweeted
Kinda relevant to this kind of an object-centric approach: arxiv.org/abs/2603.04553 (ICLR 2026)
Tal Daniel retweeted
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 retweeted
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
cambridge.org/il/universityp…
Tal Daniel retweeted
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
Excited to announce our paper is accepted to #ICML2026!
VLA robot policies are capable, but do they know when they're failing? We tackle this with TDQC: a framework for sequential uncertainty calibration, with a surprising link between calibration theory and RL value function.
Tal Daniel retweeted
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
Learning accurate World Models for long horizon planning is hard.
So what minimal aspect of world dynamics must a model capture to achieve complex goals?
We find a simple and effective solution in our #ICLR2026 paper, which we will present as an Oral at @worldmodel_26.
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Tal Daniel retweeted
Check out our latest post on CMU@ICLR 2026!
blog.ml.cmu.edu/2026/04/20/c…
Tal Daniel retweeted
🚀 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 retweeted
Code, datasets and checkpoints are now available!
github.com/DanHrmti/HECRL
#WorldModels #Diffusers #OfflineGCRL #HierarchicalRL #EntityCentricRL
Learning accurate World Models for long horizon planning is hard.
So what minimal aspect of world dynamics must a model capture to achieve complex goals?
We find a simple and effective solution in our #ICLR2026 paper, which we will present as an Oral at @worldmodel_26.
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Tal Daniel retweeted
📢 New Sponsor!
🙏Proud to have @LambdaAPI sponsoring WMW26.
💻Beyond financial support, Lambda will provide compute for attendees during tutorial sessions to enable hands-on work.
See you at WMW26!
Tal Daniel retweeted
A closed door looks the same whether it pushes or pulls. Two identical-looking boxes might have different center of mass. How should robots act when a single visual observation isn't enough?
Introducing HAVE 🤖, our method that reasons about past interactions online! #CORL2025
Tal Daniel retweeted
🚨Introducing SPOT: Search over Point Cloud Object Transformations. SPOT is a combined learning-and-planning approach that searches in the space of object transformations.
Website: planning-from-point-clouds.g…
Paper: arxiv.org/abs/2509.04645
Code: github.com/kallol-saha/SPOT
Tal Daniel retweeted
Ever wish a robot could just move to any goal in any environment—avoiding all collisions and reacting in real time?
🚀Excited to share our #CoRL2025 paper, Deep Reactive Policy (DRP), a learning-based motion planner that navigates complex scenes with moving obstacles—directly from point cloud input.
w/ @Jiahui_Yang6709
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Tal Daniel retweeted
We’ve all seen humanoid robots doing backflips and dance routines for years.
But if you ask them to climb a few stairs in the real world, they stumble!
We took our robot on a walk around town to environments that it hadn’t seen before. Here’s how it works🧵⬇️
Tal Daniel retweeted
Modern AI is confined to the digital world.
At Skild AI, we are building towards AGI for the real world, unconstrained by robot type or task — a single, omni-bodied brain. Today, we are sharing our journey, starting with early milestones, with more to come in the weeks ahead.
Our Mission: Artificial General Intelligence grounded in the physical world.
We believe AGI that can truly understand and reason in the real world can only be built through grounding in the physical world.
Our Vision: Any robot, Any task, One brain.
We tackle robotics in its full generality – building a continually improving, omni-bodied brain that can control any hardware for any task.
Who are we? A passionate group of scientists & engineers driven by our shared vision.
We have been researching AI and robotics for more than a decade. Our team includes pioneers of self-supervised learning, curiosity-driven exploration, end-to-end sim2real for visual locomotion, dexterous manipulation, learning from human videos, robot parkour, and many more. Many of these works have won awards at top-tier AI and Robotics conferences. Our team has also built production-ready systems at Anduril, Tesla, Nvidia, Meta, Kitty Hawk, Google, Everyday Robotics, and Amazon.
Join us in our mission to build the robot brains of tomorrow.
Tal Daniel retweeted
Want robot imitation learning to generalize to new tasks? Blindfold your human demonstrator!
Best robotics paper at EXAIT Workshop #ICML2025
openreview.net/forum?id=zqfT…
Wait, why does this make sense?
Read below!
Tal Daniel retweeted
Would you trust a bandit algorithm to make decisions on your health or investments? Common exploration mechanisms are efficient but scary.
In our latest work at @icmlconf, we reimagine bandit algorithms to get *efficient* and *interpretable* exploration.
A 🧵 below
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