What missing in RL based humanoid controller from industrial robots are precision and force control. CHIP can do both. We propose a simple recipe to build humanoid impedance controller, which can be used for wiping, carrying large objects and multi-robot collaboration.

🪜 What if humanoids could climb ladders and work on them straight out of simulation? Meet LadderMan: a perceptive system for zero-shot sim-to-real ladder climbing and on-ladder manipulation. Watch the humanoid climb, stabilize, and manipulate—all in one system. 🤖👇

As dexterous hand become more human like , using human data also become easier than ever, we explore how to cotrain with both human and robot data and generalize to new task with only human data.

Humanoid robotics is hitting a data wall. Teleop and mocap took us far, but they don’t scale to every object, terrain, and behavior. We’re releasing GRAIL: research.nvidia.com/labs/dai… — a fully digital pipeline for generating loco-manipulation data before the robot moves. 🧵(1/8)

What is missing to bring real-time motion research into AAA games and real-world robotics? We present MotionBricks, a step toward bridging this gap with two key components: - a single generative latent motion backbone covering 350,000 motion skills, running at 15,000 FPS with 2 ms latency and substantially improved quality and reliability. - a unified smart primitive interface for locomotion, object / scene interaction, with fine-grained control over generated behaviors. Webpage: nvlabs.github.io/motionbrick… Code: github.com/NVlabs/GR00T-Whol… Paper: arxiv.org/abs/2604.24833 (ACM TOG / SIGGRAPH 2026)

The Movement Lab has a new website ✨ A look at what we've been working on: humanoid robots, physics-based animation, and robot learning. tml.stanford.edu

Ego centric data for navigation is the way to go!

Happy Monday! More exciting SONIC releasing incoming.

Can we learn whole-body mobile manipulation directly from human demonstrations? Introducing Whole-Body Mobile Manipulation Interface (HoMMI) Egocentric UMI, 0 teleop -> bimanual & whole-body manipulation, long-horizon navigation, active perception hommi-robot.github.io

Excited to release Minimalist Compliance Control! We achieve robust, compliant robot interaction across robot arms, dexterous hands, and humanoids, with NO force sensors or learning. If you’re wondering what remains, please see the thread below😉 Website: minimalist-compliance-contro…

Generalist for dex manipulation!

Finally everyone can benefit from the result of 700h motion 128 GPU training!

We have seen rapid progress in humanoid control — specialist robots can reliably generate agile, acrobatic, but preset motions. Our singular focus this year: putting generalist humanoids to do real work. To progress toward this goal, we developed SONIC (nvlabs.github.io/GEAR-SONIC/), a Behavior Foundation Model for real-time, whole-body motion generation that supports teleoperation and VLA inference for loco-manipulation. Today, we’re open-sourcing SONIC on GitHub. We are excited to see what the community builds upon SONIC and to collectively push humanoid intelligence toward real-world deployment at scale. 🌐 Paper: arxiv.org/abs/2511.07820 📃 Code: github.com/NVlabs/GR00T-Whol…

Just like in the video, the year of humanoid is unstoppable!

Long-tail scenarios remain a major challenge for autonomous driving. Unusual events—like accidents or construction zones—are underrepresented in driving data, yet require semantic and commonsense reasoning grounded in control. We propose SteerVLA, a framework that uses VLM reasoning to steer a driving policy via grounded, fine-grained language instructions. Paper: arxiv.org/abs/2602.08440 Website: steervla.github.io/

This is amazing! Agility and adaptive compliant usually doesn’t come hand in hand, glad GentleHumanoid also make it works.

How can robots handle fragile, soft everyday objects like humans do, using vision & tactile to regulate force? 🤖🥚 Introducing our full-stack solution: a low-cost ($150) force gripper (0.45~45N), a force-aware teleoperator, and a reactive policy for learning force control.

Humanoid robots are agile but stiff. CHIP shows how to add adaptive compliance without breaking motion tracking. A single controller handles wiping, door opening, box lifting, writing, and even running while carrying objects.

🚀 Introducing CHIP: Adaptive Compliance for Humanoid Control through Hindsight Perturbation! Current humanoids face a trade-off: they are either Agile & Stiff OR Slow & Soft. CHIP breaks this barrier. We enable on-the-fly switching between Compliant (wiping 🧼, collaborative holding 📦) and Stiff (lifting dumbbells 🏋️, opening doors 🚪💪) behaviors—all while maintaining agile skills like running! 🏃💨 Website: nvlabs.github.io/CHIP/ Join me for a deep dive on how CHIP enables adaptive control for complex tasks. 🧵↓

Robust humanoid perceptive locomotion is still underexplored. Especially when different cameras see different terrains, paths get narrow, and payloads disturb balance... Introduce RPL, tackling this with one unified policy: • Challenging terrains (slopes, stairs and stepping stones); • Multiple directions; • Payloads; Trained in sim. Validated long-horizon in the real world. Watch the robot walk it all🦿 Details below👇