Roberto
@RobobertoMM
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Assistant CS Professor at UT Austin. Former Stanford and TUBerlin. Researching at the intersection of vision, learning and robotics 🏳️🌈
Joined August 2019
We are excited to release MoMaGen, a data generation method for multi-step bimanual mobile manipulation. MoMaGen turns 1 human-teleoped robot trajectory into 1000s of generated trajectories automatically.🚀 Website: https://t.co/DYKvqY4bII arXiv: https://t.co/lDffi0FXHl
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A little late to this but excited to share that DataMIL won the best paper at the Data workshop at #CoRL! If you haven't already, check it out! 👇
Ever wondered which data from large datasets (like OXE) actually helps when training/tuning a policy for specific tasks? We present DataMIL, a framework for measuring how each training sample influences policy performance, hence enabling effective data selection 🧵
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🚨CoRL 2025 Best Poster Award 🏆 Paper Alert 🚨 ➡️Paper Title: Mash, Spread, Slice! Learning to Manipulate Object States via Visual Spatial Progress 🌟Few pointers from the paper 🎯Most robot manipulation focuses on changing the kinematic state of objects: picking, placing,
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Excited that SPARTA ( https://t.co/AVZTpsbfSw) won the best poster award at the CoRL RINO workshop! Big congrats to the project lead Priyanka, who worked so hard on this project, as well as to the rest of the co-authors @ShivinDass @sagnikmjr @RobobertoMM and Kristen!
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📢 Call for Community Activities #AAAI2026 We invite submissions of proposals for including and open activities that help broaden community participation in the AI field. October 4: Submission Deadline October 18: Acceptance Notifications @RobobertoMM @marucabrera27 @RealAAAI
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Simple but *so effective idea*! And it can be used with any feature data selector. Great work led by @sateeshk21 . Do not miss it at #CoRL2025 (Spotlight 4 & Poster 2 on Sept 29)!
Which data is best for training few-shot imitation policies for robot manipulation? Some think it’s the data that looks similar, or has similar motion, or comes with related language labels. They are all right AND wrong: depending on the task, sometimes this similarity helps but
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Intelligent humanoids should have the ability to quickly adapt to new tasks by observing humans Why is such adaptability important? 🌍 Real-world diversity is hard to fully capture in advance 🧠 Adaptability is central to natural intelligence We present MimicDroid 👇 🌐
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Honored to give an Early Career Invited Talk at #IJCAI today. See you at 11:30am in room 520C!
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It was time to improve our evaluations in robot learning! We introduce a methodology based on anonymous A/B testing: fairer, stronger, community-driven. Awesome work by @KarlPertsch @pranav_atreya @tonyh_lee and an incredible crowdsourcing team. Upload and test your model! 🚀
We’re releasing the RoboArena today!🤖🦾 Fair & scalable evaluation is a major bottleneck for research on generalist policies. We’re hoping that RoboArena can help! We provide data, model code & sim evals for debugging! Submit your policies today and join the leaderboard! :) 🧵
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Imagine a future where robots are part of our daily lives — How can end users teach robots new tasks by directly showing them, just like teaching another person? 🧵👇
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Meet Casper👻, a friendly robot sidekick who shadows your day, decodes your intents on the fly, and lends a hand while you stay in control! Instead of passively receiving commands, what if a robot actively sense what you need in the background, and step in when confident? (1/n)
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🚨RL training for contact-rich tasks with a mobile manipulator IN THE REAL WORLD?!🤯 We're not crazy—just equipped with the right action space! SLAC learns a safe, effective action space via unsupervised RL in sim, enabling real-world RL training in minutes. Check it out!🚀
Real-world RL, where robots learn directly from physical interactions, is extremely challenging — especially for high-DoF systems like mobile manipulators. 1⃣ Long-horizon tasks and large action spaces lead to difficult policy optimization. 2⃣ Real-world exploration with
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Excited to be in ATL for #ICRA2025 to present 🔥FLaRe: fine-tuning large transformer policies with #RL, 15:25 Tuesday @ room 410! I will also be attending the 📷Doctoral Consortium on Monday to talk about my research on self-improving robots. Happy to meet old and new friends!
🚀 Despite efforts to scale up Behavior Cloning for Robots, large-scale BC has yet to live up to its promise. How can we break through the performance plateau? Introducing 🔥FLaRe: fine-tuning large-scale robot policies with Reinforcement Learning. https://t.co/iRC1NTgoFI 🧵
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Loved working on this with our MIT/Stanford/OpenAI collaborators. It brings "The Bitter Lesson" to data curation: skip the hand-tuned heuristics (visual similarity, motion...) and let the data speak for itself! Datamodels is a fascinating framework 🤯
Ever wondered which data from large datasets (like OXE) actually helps when training/tuning a policy for specific tasks? We present DataMIL, a framework for measuring how each training sample influences policy performance, hence enabling effective data selection 🧵
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Extremely excited to announce that I will be joining @UTAustin @UTCompSci in August 2025 as an Assistant Professor! 🎉 I’m looking forward to continuing to develop AI agents that interact/communicate with people, each other, and the multimodal world. I’ll be recruiting PhD
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So happy for @JiahengHu1 ! He has been rocking it, with outstanding work that pushes the limits of what robot learning can achieve in mobile manipulation and other domains. And one of my first Ph.D. students! Congratulations! 🦾🦾🦾🦾
I'm honored to be awarded the 2025 Two Sigma PhD fellowship, and extremely grateful to my two amazing advisors @RobobertoMM @PeterStone_TX ! Looking forward to continuing to advance the field of RL and Robotics.
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✨Super excited to share what the team has been working on! ♊️🤖 Gemini Robotics is a family of frontier models that are dexterous, interactive, and general. It builds on top of Gemini's world understanding, enhancing it's spatial/embodied reasoning, and producing robot
Meet Gemini Robotics: our latest AI models designed for a new generation of helpful robots. 🤖 Based on Gemini 2.0, they bring capabilities such as better reasoning, interactivity, dexterity and generalization into the physical world. 🧵 https://t.co/EXRJrmxGxl
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Giving a talk as New Faculty Highlight at AAAI tomorrow morning (9:30am)! https://t.co/gzhs00f4Qt Come if you want to get an overview of some of the works from the lab
aaai.org
The Thirty-Ninth AAAI Conference on Artificial Intelligence will be held in Philadelphia at the Pennsylvania Convention Center in 2025.
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Tired of guessing what tasks people want robots to do for them? Check our study! We correlate time spent and emotions people felt while performing tasks with the desire to automate them, comparing between different groups. And with an online tool for you to play with the data!
🤔What tasks do we want robots to handle? Are these preferences based on saved time or feelings we associate with the tasks? Introducing Why Automate This?—a study exploring automation preferences across social groups, using feelings & time-spent as key factors. 👇 (1/5)
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In multi-object env, why do most Unsupervised Skill Discovery methods fail to learn complex skills like tool use? Because they simply maximize state coverage. Introducing our solution SkiLD: Skill Discovery Guided by Factor Interactions (NeurIPS24) https://t.co/buo3qSdI1O
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