❓How can humanoids learn to squat and open a drawer? Reward-tuning for every such whole-body task is infeasible. 🚀Meet DreamControl: robots "dream" how people move and manipulate objects in varied scenarios, practice using them in simulation, and then act naturally in the

Introducing 🚀 Humanoid Everyday — a large, real-world dataset for humanoid whole-body manipulation. Unlike most humanoid data (fixed bases, narrow tasks), ours covers diverse, locomotion-integrated skills. 🔗 Website: https://t.co/0wmXltt13R 📄 Paper: https://t.co/lt8V6HZIO3

how diffusion models work

💡 How can humanoids achieve autonomous, generalizable, and lifelike interactions with real-world objects? 🤖 Introducing PhysHSI: Towards a Real-World Generalizable and Natural Humanoid-Scene Interaction System Paper: https://t.co/k7iykFXjn8 Website: https://t.co/eC3oQvsLKt

I've long wondered if we can make a humanoid robot do a 𝘄𝗮𝗹𝗹𝗳𝗹𝗶𝗽 - and we just made it happen by leveraging 𝗢𝗺𝗻𝗶𝗥𝗲𝘁𝗮𝗿𝗴𝗲𝘁 with BeyondMimic tracking! This came after our original OmniRetarget experiments, with only minor tweaks to RL training: relaxing a

Published my personal notes from interview prep at top AI labs like OpenAI, Anthropic, WorldLabs, etc, revisiting core concepts in large-scale ML design and optimization for efficient model training and inference. Hope it’s useful to others exploring and deep-diving into ML/LLMs

🎙️ @akapoor_av8r is building @genrobotics_ai to solve the biggest deployment problem in robotics: Getting real robots to work in the real world. In this episode, he shares how he’s doing it, and why most robotics stacks aren’t built to scale. We talk about growing up in India,

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Much needed!

Implementing motion imitation methods involves lots of nuisances. Not many codebases get all the details right. So, we're excited to release MimicKit! https://t.co/7enUVUkc3h A framework with high quality implementations of our methods: DeepMimic, AMP, ASE, ADD, and more to come!

ResMimic: a two-stage residual framework that unleashes the power of pre-trained general motion tracking policy. Enable expressive whole-body loco-manipulation with payloads up to 5.5kg without task-specific design, generalize across poses, and exhibit reactive behavior.

✈️🤖 What if an embodiment-agnostic visuomotor policy could adapt to diverse robot embodiments at inference with no fine-tuning? Introducing UMI-on-Air, a framework that brings embodiment-aware guidance to diffusion policies for precise, contact-rich aerial manipulation.

Figure 03 coming 10/9

This is real wtf:

You can now chat with apps in ChatGPT.

#TTT3R: 3D Reconstruction as Test-Time Training We offer a simple state update rule to enhance length generalization for #CUT3R — No fine-tuning required! 🔗Page: https://t.co/HmEvUwMgLJ 1/4 We rebuilt @taylorswift13’s "22" live at the 2013 Billboard Music Awards—in 3D

Last year, I came across the idea of constrained decoding (I know, late to the party) and was fascinated. The ability to enforce constraints for LLMs at inference time without fine tuning is a powerful idea. It got me thinking, can we do this for robot foundation models? 1/n🧵

CoRL 2026 is cutting review cycles to <3 months to keep pace with the rapid progress in robotics. @ieee_ras_icra @IROS2025 — maybe it’s time to rethink too. It makes little sense to discuss papers at conferences a year after submission, when they’re already outdated.

Humanoid motion tracking performance is greatly determined by retargeting quality! Introducing 𝗢𝗺𝗻𝗶𝗥𝗲𝘁𝗮𝗿𝗴𝗲𝘁🎯, generating high-quality interaction-preserving data from human motions for learning complex humanoid skills with 𝗺𝗶𝗻𝗶𝗺𝗮𝗹 RL: - 5 rewards, - 4 DR

Agents have transformed software, now it's time for robotics. Today, we’re revealing our blueprint for building agentic robots –– machines that can reason, converse, compose, and remember. https://t.co/1bmR9vz3lJ

I'm super excited to announce mjlab today! mjlab = Isaac Lab's APIs + best-in-class MuJoCo physics + massively parallel GPU acceleration Built directly on MuJoCo Warp with the abstractions you love.