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A robot should have softness and many sensors to manipulate an object dexterously and to adapt various environments. However, many existing schemes where a designer calibrates the sensor output to the world coordinate frame are difficult to adapt for such the robot. This paper proposes a learning mechanism for a robot hand which consists of anthropomorphic fingertips. The sensor for the fingertip is difficult to calibrate because the sensor receptors are embedded randomly in the soft material. The effectiveness of the proposed mechanism is demonstrated by an experiment that the robot picks up an unknown weight object.