Active object perception using Bayesian classifiers and haptic exploration

To recognise objects using only tactile sensing, humans employ various haptic exploratory procedures (EPs). Because the time, effort, and information acquisition costs of different EPs vary, choosing the best EP for accurate and efficient perception is usually based on prior knowledge or experience, also known as active exploration. An active EP selection algorithm based on a Gaussian mixture modal and Bayesian classifier has been developed to empower robots with similar intelligence. To choose the best EP for the next perception iteration, the information gain and total time cost of all actions required to identify the object are both considered. Six EPs were realised using a designed robotic arm platform, allowing eight features representing the object's surface and geometric properties to be extracted. To evaluate the algorithm, offline data and real-world experiments were used, with the random method as a comparison. According to the results, the active method outperformed the random method with higher accuracy and in significantly less time. It had an average of weighted information gain of 132.6 and a time cost ratio (spent/total time) of only 0.3.