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

Open-source: complete codebase covering multiple simulation backends, training, retargeting, and real-world inference. Infra built for humanoid, but also readily modified for quadruped (also included). Lots of infra gems/conveniences we rely on consistently. Hopefully equally

Sim-to-real learning for humanoid robots is a full-stack problem. Today, Amazon FAR is releasing a full-stack solution: Holosoma. To accelerate research, we are open-sourcing a complete codebase covering multiple simulation backends, training, retargeting, and real-world

Meet BFM-Zero: A Promptable Humanoid Behavioral Foundation Model w/ Unsupervised RL👉 https://t.co/3VdyRWgOqb 🧩ONE latent space for ALL tasks ⚡Zero-shot goal reaching, tracking, and reward optimization (any reward at test time), from ONE policy 🤖Natural recovery & transition

one of the best moments at BAIR lab actually never imagined to spot prof @redstone_hong on a random day

Excited to introduce TWIST2, our next-generation humanoid data collection system. TWIST2 is portable (use anywhere, no MoCap), scalable (100+ demos in 15 mins), and holistic (unlock major whole-body human skills). Fully open-sourced: https://t.co/fAlyD77DEt

How can we standardize conditioning signals in image/video models to achieve the iterative editing & portability that Universal Scene Descriptors provide in graphics? Introducing Neural USD: An object-centric framework for iterative editing & control 🧵

Simulation drives robotics progress, but how do we close the reality gap? Introducing GaussGym: an open-source framework for learning locomotion from pixels with ultra-fast parallelized photorealistic rendering across >4,000 iPhone, GrandTour, ARKit, and Veo scenes! Thread 🧵

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.

We are open-sourcing over 4 hours of high-quality, retargeted trajectories! Website: https://t.co/EeW71PaXVI ArXiv: https://t.co/8jxK1svcmT Datasets: https://t.co/xUq8seOxtM Huge shout out to the amazing team: @lujieyang98, @x_h_ucb, @akanazawa, @pabbeel, @carlo_sferrazza,

Standing on the shoulders of giants! Our work builds on amazing research in the community💡. We use the "interaction mesh" 🕸️ [1], [2] to preserve spatial relationships and leverage the minimal RL formulation from works like BeyondMimic [3]. Our long-horizon sequence is a nod to

Our grand finale: A complex, long-horizon dynamic sequence, all driven by a proprioceptive-only policy (no vision/LIDAR)! In this task, the robot carries a chair to a platform, uses it as a step to climb up, then leaps off and performs a parkour-style roll to absorb the landing.

But how much better is our data? 🤔 Compared to widely-used baselines, our motions show far fewer physical artifacts—virtually zero foot-skating and penetration—while better preserving contact. This allows us to use an open-sourced RL framework (BeyondMimic) without

And it's not just for a specific robot! Our framework is highly general and adapts to different robot embodiments, including the @UnitreeRobotics H1 and the @boosterobotics T1. We can retarget complex object-carrying and platform-climbing skills across these different robots with

What about scalability? OmniRetarget transforms a SINGLE human demo into diverse motion clips. We can systematically vary terrain height, object size, and initial poses. Best of all, these augmented skills transfer directly from sim to our real-world hardware! 🤖➡️🦾 4/9

The result of this high-quality data? We can train diverse skills like box carrying 📦, slope crawling 🐾, and platform climbing 🧗 with a radically simplified RL process! All policies use just 5 reward terms, achieving successful zero-shot sim-to-real transfer! 🎯➡️🦾 3/9

Existing retargeting often produces artifacts like foot-skating and penetration ❌. To compensate, RL policies rely on complex ad-hoc reward terms, forcing a trade-off between accurate motion tracking and correcting errors like slipping or bad contacts. OmniRetarget fixes this

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

Excited to share that I'll be joining @UTAustin in Fall 2026 as an Assistant Professor with @utmechengr @texas_robotics! I'm looking for PhD students interested in humanoids, dexterous manipulation, tactile sensing, and robot learning in general -- consider applying this cycle!

We're hiring interns (and full-times) all year long! Please email me if interested.