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We present an approach to dextrous robot

grasping which combines a purely tactile-driven reactive

algorithm with an implicit representation of grasp experience

to yield an algorithm which can handle arbitrary, partially

unknown grasp situations, i.e. vague object shape and

position. During the grasp movement, the obtained contact

information is used to dynamically adapt the grasping control

by targeting the best matching posture from the experience

base. Thus, the robot recalls and actuates a grasp

it already successfully performed in a similar tactile context.

To efficiently represent the experience, we introduce

the Grasp Manifold assuming that grasp postures form a

smooth manifold in hand posture space. We present a simple

way of providing approximations of Grasp Manifolds

using Self-Organising Maps (SOMs) and study the properties

of the represented grasp manifolds concerning their

smoothness and robustness against clustered training data.