Giving a robot model a sense of touch lifted its success to 72.5%.
Chinese firm Shengshu released Motus2 on Saturday, software that plans and predicts a robot hand's moves. The company says adding touch feedback raised success on paper-cup tasks from 60% to 72.5%. No outside lab has confirmed the figures, so treat them as the company's own claim.
Why it mattersMotus2 does three jobs at once, acting, predicting the outcome and grading its own attempt. That loop is how a robot could get better at a chore without a human retraining it.
▶ Related video: Yunzhu Li - Scaling Robotic Manipulation via Structured World Models and Tactile Sensing | Montreal Robotics
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