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Autonomous exploration in robotics is a crucial feature to achieve robust and safe systems capable to interact with and recognize their surrounding environment. In this paper, we present a method for object recognition using a three-fingered robotic hand actively exploring interesting object locations to reduce uncertainty. We present a novel probabilistic perception approach with a Bayesian formulation to iteratively accumulate evidence from robot touch. Exploration of better locations for perception is performed by familiarity and novelty exploration behaviors, which intelligently control the robot hand to move toward locations with low and high levels of interestingness, respectively. These are active behaviors that, similar to the exploratory procedures observed in humans, allow robots to autonomously explore locations they believe that contain interesting information for recognition. Active behaviors are validated with object recognition experiments in both offline and real-time modes. Furthermore, the effects of inhibiting the active behaviors are analyzed with a passive exploration strategy. The results from the experiments demonstrate the accuracy of our proposed methods, but also their benefits for active robot control to intelligently explore and interact with the environment. [ABSTRACT FROM AUTHOR]

In this paper, a pose-invariant hand shape recognition method based on the geometry of the fingers is proposed. Firstly, inspired by the segmentation method presented by Yoruk et al., we conduct a novel improvement on the segmentation for extracting the region of the fingers when the hand is in a natural pose. Secondly, Fourier descriptors and finger area functions are employed to extract the finger boundary curve features and region areas, respectively. Finally, score-level fusion based on a weighted sum is used to obtain matching results. Because the finger segmentation strategy and the feature extraction method are both rotation and translation invariant, the proposed method is more suitable for a naturally posed hand. Experiments using the Bogazici University Hand database show that the proposed method can achieve an equal error rate of 0.0369 for all data and 0.0273 for samples with an intragroup angle deviation of less than 45°. Thus, the proposed method is suitable for real-world applications. [ABSTRACT FROM PUBLISHER]