{"id":121596,"date":"2026-03-13T11:15:08","date_gmt":"2026-03-13T14:15:08","guid":{"rendered":"https:\/\/ces.einnews.com\/article\/899178561"},"modified":"2026-03-13T11:15:08","modified_gmt":"2026-03-13T14:15:08","slug":"ai-robotics-unicorn-sharpa-and-nvidia-bridge-the-simulation-gap-for-dexterous-robot-training","status":"publish","type":"post","link":"https:\/\/new7.shop\/zerocostfreehost\/index.php\/2026\/03\/13\/ai-robotics-unicorn-sharpa-and-nvidia-bridge-the-simulation-gap-for-dexterous-robot-training\/","title":{"rendered":"AI Robotics Unicorn Sharpa and NVIDIA Bridge the Simulation Gap for Dexterous Robot Training"},"content":{"rendered":"
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MOUNTAIN VIEW, Calif.–(BUSINESS WIRE)–Mar 13, 2026–<\/p>\n

Sharpa<\/a> presents new research demonstrating significant improvements in simulation methods for robot training, in collaboration with NVIDIA.<\/p>\n

This press release features multimedia. View the full release here: https:\/\/www.businesswire.com\/news\/home\/20260313513557\/en\/<\/a><\/p>\n

Sharpa aims to accelerate the deployment of robots capable of complex manipulation tasks across consumer and enterprise markets. To achieve human-like dexterity, Sharpa leverages simulation either by training the robot on single tasks using Reinforcement Learning (RL) in virtual environments; or by generating synthetic data used to pre-train its Vision Tactile Language Model (VTLA). Standard tactile simulations typically force a choice between physical authenticity and computation speed. Tacmap, a simulation framework developed in cooperation with NVIDIA, solves this issue through a shared, high-fidelity geometric representation. The simulation and code assets will be open sourced to spread the learnings with the broader robotics community.<\/p>\n

\u201cThis collaboration strengthens the foundation for training in simulation, advancing the robotics field towards more dexterity and autonomy and accelerating large-scale deployment,\u201d said Alicia Veneziani, Global VP of Go-To-Market and President of Europe at Sharpa.<\/p>\n

In addition, Sharpa is proud that its dexterous hand Wave<\/em> has been chosen by NVIDIA to advance research on data efficient learning. NVIDIA’s GEAR Lab researchers successfully transferred policies, obtained from pre-training GR00T model on 20,000+ hours of human videos, to robots equipped with Sharpa\u2019s Wave<\/em> hands. The robots were able to complete tasks like assembling model cars, operating syringes, and sorting cards with a 54% higher success rate, proving that video data-based training can be effectively scaled on robots with highly anthropomorphic hands.<\/p>\n

Sharpa, a recipient of the iF Product Design Award, and CES Innovation 2026 Award, will present its dexterity-first, full stack solutions at GTC 2026, demonstrating how human-like hands coupled with tactile VTLA can accelerate the deployment of productive robots. Sharpa is excited to announce its membership in the NVIDIA Inception program.<\/p>\n