30 September 2026 | News
Image Courtesy: Public Domain
Sharpa, an AI robotics company focused on dexterous manipulation, unveiled three flagship products at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2026: D01, an integrated robot built for dexterous manipulation; W02, a compact, lightweight dexterous hand with full-hand tactile sensing; and AE01, a haptic exoskeleton data glove for teleoperation and data capture.
The new lineup addresses three connected needs in robotics development: executing coordinated physical tasks, sensing and controlling contact with objects, and collecting human demonstrations to support robot learning. Together, the products expand Sharpa's hardware portfolio for developing robots that can use everyday tools and work in environments designed for people.
D01: the First Integrated Tactile-sensing Robot Built for Dexterous Manipulation
D01 brings together Sharpa Wave dexterous hands, seven-degree-of-freedom arms and whole-body electronic skin.
Built to combine strength, speed and precision, D01 offers an approximately 1:1 arm payload-to-weight ratio, a maximum end-effector speed exceeding 10.5 meters per second, a communication frequency of 1,000 Hz and end-effector repeatability of 0.2 millimeters. Human-scale spherical wrist joints support manipulation in workspaces and around tools designed for people.
D01's electronic skin extends contact sensing across the body, with a tactile sampling rate of 100 Hz and force resolution of 0.2 newtons. This additional feedback enables the robot to detect physical contact across its body and supports responsive interaction with objects and its surroundings.
W02: a Next-generation, Fully tactile, Ultra-compact and Lightweight Dexterous Hand
Building on Sharpa's W01 dexterous hand, W02 combines 21 active degrees of freedom with a compact structure weighing less than 750 grams. Its smaller, lighter design reduces loading on the robot arm and supports integration into workspaces built around human-hand dimensions.
W02 combines Sharpa's proprietary Dynamic Tactile Array at the fingertips with electronic skin across the fingers and palm. Fingertip sensors provide a force sensing range of 5 millinewtons to 30 newtons and spatial resolution of 1 millimeter. The electronic skin extends tactile coverage with a force sensing range of 0.1–20 newtons and spatial resolution of 5 millimeters.
The hand's zero-gap grasping capability supports the handling of slender objects such as chopsticks and fine cords. IP54-rated protection against dust and water splashes supports use in varied working environments. These features are designed to help robots handle everyday objects and tools while maintaining tactile feedback throughout the interaction.
AE01: a High-fidelity Haptic Exoskeleton Data Glove
AE01 is a high-fidelity haptic exoskeleton data glove designed for robot teleoperation and egocentric data capture. Its 22-degree-of-freedom design incorporates 22 encoders to capture joint motion, providing movement data for real-time robot control and the collection of manipulation demonstrations.
AE01 provides 256 levels of dynamic vibrotactile feedback at each fingertip, giving operators tactile cues to support adjustments during teleoperation. It also offers fingertip position repeatability of less than 1 millimeter.
The glove requires no additional user calibration and is designed to resist environmental interference, simplifying setup and supporting data collection across different operating environments. By combining motion capture with haptic feedback, AE01 gives researchers and developers a practical interface for guiding robotic actions and collecting training data.
Advancing Dexterous Manipulation to Real-World Tasks
With D01, W02 and AE01, Sharpa is expanding the tools available to develop, control and train robots for tasks involving sustained physical contact. The portfolio supports the company's broader goal of making robotic manipulation useful across increasingly complex workflows, from handling individual objects to completing connected tasks in real-world environments.
