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We present a vision-based robotic system which uses a combin ation of several active sensing strategies to grip a free-standing small target object with an initially unkno w position and orientation. The object position is determined and maintained with a probabilistic visual trac king system. The cameras on the robot contain a motorized zoom lens, allowing the focal lengths of the camer as to be adjusted during the approach. Our system uses an entropy-based approach to find the optimal zoom level s for reducing the uncertainty in the position estimation in real-time. The object can only be gripped effic i ntly from a few distinct directions, requiring the robot to first determine the pose of the object in a classifi cat on step, and then decide on the correct angle of approach in a grip planning step. The optimal angle i s trained and selected using reinforcement learning, requiring no user-supplied knowledge about the o bj ct. The system is evaluated by comparing the experimental results to ground-truth information.

ACTIVE SENSING STRATEGIES FOR ROBOTIC PLATFORMS, WITH AN APPLICATION IN VISION-BASED GRIPPING