Check out our recent work accepted to #RSS2026! We enable a robot to learn a flying knot from a single human demonstration and less than 10 trials using Task-Level Iterative Learning Control: flying-knots.github.io/

Everyone asks if Atlas can bring them a drink, but this robot can bring you the whole fridge. Using AI-driven behaviors, Atlas is doing hard work and coordinating its whole body to manage heavy objects, balancing complex contact points with accuracy and reliability.

GR00T-VisualSim2Real is now open source! VIRAL and DoorMan are now available with training code, simulation assets, and the full recipe for bringing visual sim-to-real loco-manipulation skills to your own humanoids. Repo: github.com/NVlabs/GR00T-Visu…

Watch AthenaZero juggle barehanded using on-board sensory feedback only. No motion capture. No funnels. No help adding the third ball. The robot learns to adapt to the uncertainties from contact and the appropriate hand-eye coordination. Learn more: rai-inst.com/resources/blog/…

When roboticists think about where robots can best fit into our daily lives, there is often a focus on "Dull, Dirty, and Dangerous" (DDD) jobs. But what if those terms aren't as straightforward as they sound? Before we automate, we need to understand the whole picture for this type of work. We explore the social science behind DDD work and offer a framework to better understand the context of DDD jobs: rai-inst.com/resources/blog/…

Let your robots hear slips with A-SLIP! 🤖🎧 How can a robot detect in-hand slip and estimate its direction and magnitude without cameras or fragile tactile skins? A-SLIP uses piezoelectric microphones embedded in grippers to hear it. a-slip.github.io/ 🧵1/7

Excited to share SoftAct, a framework for retargeting human manipulation demos to soft robot hands using explicit contact force reasoning! How do you transfer human skill to a hand that looks and moves nothing like yours🐙🖐️? It turns out VR environments can let us capture privileged force interaction demonstrations to help. 🧵1/7

Learning from human videos often requires restrictive, carefully choreographed human motions. We propose ✨3PoinTr✨: a scalable way to pretrain from casual human videos. It bridges the embodiment gap by learning 3D scene evolution, enabling learning from natural human motions.

Why does manipulation lag so far behind locomotion? New post on one piece we don't talk about enough: The gearbox. The Gap You've probably seen those dancing humanoid robots from Chinese New Year. Locomotion isn't entirely solved; but clearly it's on a trajectory. But we haven't seen anything close for manipulation. 𝗪𝗵𝘆? When sim-to-real transfer fails, the instinct is to blame the algorithm. Train bigger networks. Crank up domain randomization. Those approaches have made real progress; we don't deny that. But we started wondering: are we treating the symptom or the disease? The Hardware Bottleneck: Fingers are too small for powerful motors. So most hands use massive gearboxes (200:1, 288:1) to get enough torque. But those gearboxes break everything manipulation needs:   • Stiction and backlash are complex to simulate. Policies trained on smooth physics hallucinate when they hit that reality.   • Reflected inertia scales as N². At large gear ratio, the finger hits with sledgehammer momentum.   • Friction blocks force information. The hand becomes blind. And they're the first thing to break. What we are trying to build at Origami, we cut the gear ratio from 288:1 to 15:1 using axial flux motors and thermal optimization. The transmission becomes more transparent: backdrivable, low friction, forces propagate to motor current. Early signs are encouraging. Still running quantitative benchmarks. Why Interactive? I love how Science Center uses interactive devices to explain complex ideas. I want to borrow this concept and help people understand the hard problems in robotics better visually. The post has demos where you can toggle friction, slide gear ratios, watch the sim-to-real gap widen in real-time. What's inside:   • Interactive demos (friction curves, N² scaling, contact patterns)   • Comparison table: 14 robot hands by sim-to-real gap and force transparency   • The math behind why low-ratio matters Read it here: origami-robotics.com/blog/de… We're not claiming we've solved dexterity. The deadlock has many pieces. But we think this one's foundational. Curious what you think.

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👇

CogNVS was accepted to @NeurIPSConf 2025! 🎉We are releasing the code today for you all to try: 🆕Code: github.com/Kaihua-Chen/cog-n… Paper: arxiv.org/pdf/2507.12646 With CogNVS, we reformulate dynamic novel-view synthesis as a structured inpainting task: (1) we reconstruct input views with off-the-shelf SLAM systems, (2) create self-supervised training pairs for learning to inpaint, and (3) test-time finetune to the input at inference. with @kaihuac5 and @RamananDeva

Introducing RaC: A data collection protocol that boosts data efficiency by 10x compared to some of the best imitation results. Key idea: scale recovery & correction data systematically => policies can reset retry when acting (consistent self-correct) => better performance. 🧵0/N

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 (1/N)

Our team is presenting work at the Conference on Robot Learning, @corl_conf, in Munich, Germany this week! Learn more about our accepted research — theaiinstitute.com/news/corl…

One of the benefits of working in a relatively new field is getting to hear from its founders. We finally got around to uploading the talks from our keynote speakers at #RLC2024, including the one and only Barto!

📢lots of people asked for the video of this talk, and happy it (and most* of the other @RL_Conference keynotes) are finally up! 🎉 youtube.com/watch?v=Az5BoT7l… * sadly, doina's keynote is not available as there was a technical issue when recording it 😰

dynamic whole-body locomotion and manipulation 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗼𝗳𝗳𝗹𝗶𝗻𝗲 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴. very simple online sampling with mppi is all you need!! website: whole-body-mppi.github.io/ arxiv: arxiv.org/abs/2409.10469

🎉 Diffusion-style annealing sampling-based MPC can surpass RL, and seamlessly adapt to task parameters, all 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴-𝗳𝗿𝗲𝗲! We open sourced DIAL-MPC, the first training-free method for whole-body torque control using full-order dynamics 🧵 lecar-lab.github.io/dial-mpc…

For more detail: Check out our website (w/ code on github!): greedyperspectives.github.io… The paper is on arxiv: arxiv.org/abs/2310.10863 Or track us down at @ieeeiros in October!