Sharpa
@SharpaRobotics • 5,577 subscribers
Sharpa is an AI robotics company dedicated to developing ultra-high performance robots and core components. We Manufacture Time by Making Robots Useful.
Videos
Your first robot-handmade ice cream🤖🍦. Delivered to you by #SharpaNorth & DQ China this August in Shanghai. Autonomous. End-to-end. Full shift. Real store. Real customers. Real orders. #Sharpa #SharpaNorth #Shanghai #SharpaWave #AIRobotics #EmbodiedAI #PhysicalAI #robots #UsefulRobots #WorldModel #Blizzard #Oreocrumbs #icecream #worldcup
Sharpa764,306 просмотров • 1 месяц назад
It’s been a massive week for embodied AI foundation models: the pace of this field is truly staggering. Throwing it back to the SimToolReal work by Kushal and Tyler Lum (Cornell & Stanford labs). In February, they achieved zero-shot tool manipulation across 24 tasks using a single RL policy and a robotic arm fitted with the SharpaWave hand. Watching the robot nail these high-speed in-hand rotations is incredible. The precision is especially impressive when you consider the policy was never trained on these specific objects or tasks. This is what solving the manipulation bottleneck looks like. ⚡️
Sharpa868,354 просмотров • 4 месяцев назад
Did you know it takes #DQChina staff a full week of training to master blending a Blizzard? It's more complicated than it looks. Now imagine North having to learn the same move. A thin paper cup faces constantly shifting torque from thick, uneven ice cream. As Humanoid Scott put it, too little grip and it slips. Too much, and it crushes. It's hard for a parallel-jaw gripper to solve this: it only pinches from two sides. Wave doesn't pinch, but wraps. A five-finger grasp spreads the load and adjusts grip in real time as torque shifts, letting North run this exact motion autonomously. #SharpaWave #SharpaNorth #DexterousHands #EmbodiedAI #PhysicalAI #AIRobotics #DQChina #Blizzard #overthehorizon Royden D'Souza @Hmorvaridi
Sharpa28,002 просмотров • 1 месяц назад
Here’s the windmill assembly demo we showed at CES 2026 — the one no one saw coming. North executes a fully autonomous, long-horizon dexterous sequence with sustained hand–eye–tactile coordination and assembly-level precision enabled by tactile feedback. It’s also robust to disturbance: you can reposition the objects, and North will still identify them and recover the task. This is powered by CraftNet (VTLA) — using tactile feedback to continuously fine-tune the last-millimeter interaction, enabling reliable execution across 30+ steps. Read more about CraftNet: #Sharpa #SharpaWave #SharpaNorth #CraftNet #System0 #CES2026
Sharpa88,136 просмотров • 8 месяцев назад
Three demos showing SharpaWave’s fine manipulation under high-fidelity teleoperation. Task No.1: putting on a trash bag. With teleoperation and tactile feedback, it finds the rim, opens it, and wraps it neatly — even on a slippery, deformable object. #Sharpa #SharpaWave #Teleoperation #Tactile #DexterousHand
Sharpa80,348 просмотров • 9 месяцев назад
Watch as Sharpa North assembles a computer autonomously! From precise positioning to screwing and wire clipping, North executes complex, contact-rich tasks powered by tactile feedback. Dexterous hands + tactile AI are what make real-world task execution possible. This is the shift from simple automation to real-world robotics. #SharpaRobotics
Sharpa28,164 просмотров • 5 месяцев назад
Before a robot can perfect assembly, it needs to learn to play. The team behind SimToolReal Kushal Tyler Lum Jeannette Bohg Prof Karen J Liu 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: #Robotics #SharpaWave #Sharpa #EmbodiedAI #DexterousManipulation #RobotLearning
Sharpa13,772 просмотров • 2 месяцев назад
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: 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 📷
Sharpa15,168 просмотров • 3 месяцев назад
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 Jim Fan, Fei-Fei Li, Jitendra MALIK, Pieter Abbeel 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:
Sharpa12,931 просмотров • 2 месяцев назад
We believe we’re the first robotics company to demonstrate a robot peeling an apple with dual dexterous human-like hands. This breakthrough closes a key gap in robotics, achieving bimanual, contact-rich manipulation and moving far beyond the limits of simple grippers. 🧵↓ Today’s AI models (VLMs) are excellent at perception but struggle with action. Controlling high-degree-of-freedom hands for tasks like this is incredibly complex, and precise finger-level teleoperation is nearly impossible for humans. Our first step was a shared-autonomy system: rather than controlling every finger, the operator triggers pre-learned skills like a “rotate apple or tennis ball” primitive via a keyboard press or pedal. This makes scalable data collection and RL training possible. How does the AI manage this? We created "MoDE-VLA" (Mixture of Dexterous Experts). It fuses vision, language, force, and touch data by using a team of specialist "experts," making control in high-dimensional spaces stable and effective. The combination of these two innovations allows for seamless, contact-rich manipulation. The human provides high-level guidance, and the robot executes the complex in-hand coordination required. This work paves the way for robots that can safely handle delicate tasks in human environments. Want the full technical details? 📄 Read the full research paper: Visit us at NVIDIA GTC Booth #1838, Hall 3 to learn more! #Robotics #AI #DexterousManipulation #VLA #NVIDIAGTC Nancy Villicaña NVIDIA GTC
Sharpa20,429 просмотров • 6 месяцев назад
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