Before a robot can perfect assembly, it needs to learn to play. The team behind SimToolReal @kushalk_ @tylerlum23 @leto__jean @KarenJLiu published another cool paper! Play2Perfect pretrains on diverse, task-agnostic play (grasp, reorient, reach, etc), then finetunes on sparse-reward assembly. Result: 33× sample efficiency vs. training from scratch, and zero-shot sim-to-real down to 0.5mm clearance. Peg insertion, screwing, multi-part assembly: all running at 60Hz, real speed, real hardware. And when a grasp slips, the policy doesn't stop, it recovers and keeps going. The Sharpa Wave responded present again ;) Project: play2perfect.github.io #Robotics #SharpaWave #Sharpa #EmbodiedAI #DexterousManipulation #RobotLearning

Project Page: ftp1-policy.github.io Arxiv: arxiv.org/abs/2606.13102 Code: github.com/michaelyuancb/ftp… Model: huggingface.co/MJJJJ1064/ftp… Dataset: modelscope.cn/datasets/Eurek…

What if the next leap in robot manipulation comes from touch, not just vision? To get there, foundation models need to understand tactile feedback the way they understand images and language. And tactile policies cannot be locked to specific hardware (that makes real-world deployments & maintenance quite complicated). FTP-1 solves both. One of the 1st foundation model for touch. 21 sensors. ~3,000 hours of data. Transfers to hardware it has never seen before. 17% on known hardware. 31% on never-seen hardware. We're proud this research led by @michaelyuancb ran on #SharpaNorth, #SharpaWave hands, and our DTC sensors. Special thanks to the teams at @Tsinghua_Uni , @UCBerkeley , @ETH , and @sjtu1896. Project page: ftp1-policy.github.io/

Introduce FTP-1, the first Generalist Foundation Tactile Policy. Enjoy FTP-1 on any tactile sensors and embodiments in your labs🤗! Pretrained on 3000 hours tactile data and 21 sensors🤖, FTP-1 learns general tactile knowledge, that can even transfer to unseen sensors 🚀! FTP-1 is distributed and evaluated by 5 global institutions, including Sharpa, UC Berkeley, Tsinghua, ETH Zurich, Shanghai Jiaotong University. We fully open-source all data and checkpoints for community usage🤗. Check the blog for more details😃! ftp1-policy.github.io/

North recently tried ironing. This week: shirt folding. 👕 Unlike ironing, folding is a skill even the most reluctant of our engineers have mastered. Notice how smooth the hand movements are. Clothes deserve some respect. 😉

One of the underappreciated factor in learning dexterity from humans is the hardware – imitating humans the way we did would be much more difficult if we didn't have a roughly human sized hand. Thank you @SharpaRobotics for your support!

1 of 3: T-Rex, a new model and dataset for tactile-reactive dexterous manipulation. Tactile is a key modality for precise dynamic control:

The dataset was collected using two Sharpa Wave hands, using our high-resolution Dynamic Tactile Arrays on each fingertip to capture the dynamic touch signals needed for contact-rich manipulation. T-Rex proves that anthropomorphic hardware and tactile-reactive software are co-dependent :) Amazing work @Dantong_Niu ! P.S. The Wave's Isaac Sim URDF/USD assets and tactile parameters are available here if you wanna take a closer look: sharpa.com/pages/downloads

Robots need to feel the world to operate in it. Most manipulation policies today are tactile-blind. They either cannot interpret high-frequency tactile signals or treat them as a static channel. And the field lacks enough touch-rich datasets to train tactile-reactive policies at scale. T-Rex was built to answer both. @Dantong_Niu and team, advised by @DrJimFan, @drfeifei, @JitendraMalikCV, @pabbeel @trevordarrell have tested whether a robot policy can react to high-frequency tactile signals the way human hands do, without giving up the generalization power of modern VLAs. The result: 65% average success rate across 12 real-world tasks. 30 absolute points over the strongest baseline. In one year, tactile VLAs have gone from promising to outperforming non-tactile baselines like pi0.5 on dexterous tasks. #Robotics #SharpaWave #Sharpa #EmbodiedAI #DexterousManipulation #TactileSensing #RobotLearning Project link: tactile-rex.github.io/

Excited to share T-Rex: Tactile-Reactive Dexterous Manipulation 🦖🤖 Touch is fundamental to human dexterity, yet most Vision-Language-Action (VLA) models either ignore tactile feedback or lack the ability to react to high-frequency contact signals. In this work, we tackle both the data and architectural challenges of tactile-reactive dexterous manipulation. 🦖 A 100-hour tactile-synchronized dexterous manipulation dataset with 7,700 trajectories, 22 motor primitives, and 200 everyday objects. 🦖 A tactile-reactive MoT architecture with spatial-temporal tactile encoding and asynchronous high-frequency tactile refinement. 🦖 A scalable training recipe combining 22,889 hours of human egocentric pretraining with tactile-grounded robot mid-training. Across 12 real-world contact-rich manipulation tasks, T-Rex achieves over 30% higher average success rate than the strongest baseline. We are fully open-sourcing the dataset, models, teleoperation stack, training code, and inference pipeline. 🌐 Project: tactile-rex.github.io/ 📄 Paper: arxiv.org/abs/2606.17055 💻 Code: github.com/ZhuoyangLiu2005/T… 🤗 Dataset: huggingface.co/datasets/zeka… 🧵 Thread ↓

Robots are the bottleneck in scaling robotics, and learning from human video promises to solve it. But how can chaotic human data ever measure up to sanitized, lab-made teleoperation data? Introducing Do as I Do: establishing a much needed correspondence between human videos and dexterous robot data. Some fun insights below: 🧵

If you want a vision encoder for dexterous manipulation, what should be the most important part to model? 🤔 Current standard models like CLIP, SigLIP, and DINOv2 have an incredible grasp of semantics and spatial details. But they lack the action-centric structure needed for downstream visuomotor control. But collecting annotated robotic trajectories at scale is SUPER expensive and largely unrealistic. So, how do we bridge this gap? We introduce CAIP (Contrastive Action-Image Pre-training) ⬇️ 🔸 Action-centric upstream: we align visual observations with action chunks through a contrastive objective. 🔸 Human video as a proxy: we represent 3D human hand poses analogously to robotic end-effector actions, tapping into a massive source of human demonstrations. 🔸 Massive scale: pre-trained on over 32,000 hours of manipulation video, driving both sample efficiency and robust generalization. 🔸 Hardware proven: achieves a 76% average success rate on a real-world Dexmate Vega bimanual @DexmateAI manipulator with dual 22-DoF Sharpa Wave hands @SharpaRobotics . 🔸 State-of-the-art: significantly outperforms strong baselines like DINOv2, SigLIP, MVP, and Qwen3.5 ViT across complex tasks, even under unexpected lighting changes and visual distractors. 🌐 Project: caip-encoder.github.io/ 📝 Blog: x.com/roeiherzig/status/2063… 📄 Paper: arxiv.org/abs/2606.17256 💻 Model: huggingface.co/yuvansharma/c…

We can use videos from the internet to teach robots! Do as I Do, from @bhawna_paliwal_ , @HarithejaE , and Willian Liang at UC Berkeley — advised by @pabbeel, @notmahi, and @JitendraMalikCV, used an algorithm that reconstructs hand-object interactions from monocular RGB video and retargets them into real, executable trajectories for multi-fingered dexterous hands. Just using "low quality" video footage of humans doing tasks. No sensors. The Sharpa Wave robot hand being anthropomorphic, it matches human kinematics. Not only that works, but at fast speed, too! Congrats to the team, that's super exciting! Project: do-as-i-do.com/ #Robotics #SharpaWave #Sharpa #EmbodiedAI #DexterousManipulation #RobotLearning

We can convert human videos to robot hand-object interaction trajectories in 4D. Enjoy! Paper: arxiv.org/abs/2606.19333 Website: do-as-i-do.com Code: github.com/malik-group/do-as… Authors:@bhawna_paliwal_,@HarithejaE,@willjhliang, @pabbeel , @notmahi , @JitendraMalikCV

#ICRA2026 highlights: - @leto__jean and the @NVIDIARobotics team featured the Sharpa Wave in their SimToolReal and EgoScale talks 🙏 - We caught up with researchers and partners from around the world - North handed out Austrian chocolates 🍫 - There were many kids in the audience. Austria starts teaching robotics early 👀 See you at RSS! #ICRA2026 #AIRobotics #Robotics #IEEE #DexterousManipulation #TactileAI #Vienna #SharpaNorth #SharpaWave #DexterousHand

Question to the community: if a robot could iron your clothes, would you wear shirts? Asking for our engineers🙃.

Touch alone isn’t enough. 🖐️ For robotics, tactile intelligence truly levels up when touch gains spatial meaning. At #ICRA2026, we dove into the core concept behind SaTA: Spatially-anchored Tactile Awareness for robust, dexterous manipulation. Read it here: arxiv.org/html/2510.14647v1. The challenge is fundamental: a robot shouldn’t just register a touch, it needs to understand exactly where that contact occurs relative to its fingers, joints, and overall hand structure. This is the missing link that turns raw data into precise, real-time adjustments during manipulation. Why does this matter? Because the most complex part of any manipulation task happens when vision is at its least reliable, that final millimeter before insertion, sliding, gripping, or fine-tuning. At Sharpa, this is exactly why we’re building tactile hands and tactile AI in tandem. 🚀 #Sharpa #Robotics #EmbodiedAI #TactileIntelligence #ICRA2026 #DexterousManipulation 📷

New: Nvidia has partnered with Chinese robotics champion Unitree on a new humanoid robot reference design called H2 that combines Unitree’s human-sized H2 robot body, Sharpa’s five-fingered hands, and Nvidia’s Isaac GR00T robotics models. scmp.com/tech/big-tech/artic…

Exciting news on GR00T: NVIDIA announces our first open humanoid robot platform, featuring Unitree H2 Plus and Sharpa hands, to accelerate academic research and facilitate cross-institutional collaboration. R&D in humanoid robotics needs broader participation. Open science is how we build the future faster, together.

The Robotic Origami Challenge is coming to IROS 2026 (Sept 26) and it's one of the most creative dexterous manipulation benchmarks we've seen. The task: train a policy to fold a traditional Japanese paper airplane. The judge: an Origami Grand Master from the Nippon Origami Association. You got #SharpaWave robot hands? Good! You don't? We will be providing real-world eval support remotely. Excited to see what you will come up with, along with the organizers @chris_j_paxton @micoolcho @DJiafei More info: robotic-origami-challenge.ne…