As humans can explore and understand the world through the sense of touch, tactile sensing is also an important aspect of robotic perception. In unstructured environments, robots can encounter both known and novel objects, this calls for a method to address both known and novel objects. In this study, we combine a particle filter (PF) and Gaussian process implicit surface (GPIS) in a unified Bayesian framework. The framework can differentiate between known and novel objects, perform object recognition, estimate pose for known objects, and reconstruct shapes for unknown objects, in an active learning fashion. By grounding the selection of the GPIS prior with the maximum-likelihood-estimation (MLE) shape from the PF, the knowledge about known objects' shapes can be transferred to learn novel shapes. An exploration procedure with global shape estimation is proposed to guide active data acquisition and conclude the exploration when sufficient information is obtained. The performance of the proposed Bayesian framework is evaluated through simulations on known and novel objects, initialized with random poses. The results show that the proposed exploration procedure, utilizing global shape estimation, achieves faster exploration than a local exploration procedure based on rapidly explore random tree (RRT). Overall, our results indicate that the proposed framework is effective and efficient in object recognition, pose estimation and shape reconstruction. Moreover, we show that a learned shape can be included as a new prior and used effectively for future object recognition and pose estimation.

C. Robustness to external noise The sound samples used in all experiments are already recorded under real-world conditions in an office environment. We took care that people in the office are not talking, but background noise like typing on a keyboard and people walking around are clearly identifiable on the sample data. In addition, there is a significant amount of ego noise coming from the robot's servos. Hence, the results depicted in the previous Sections VA and V-B already involve a realistic amount of noise. However, in order to make more precise statements about robustness to noise, we also perform experiments where we simulate an environment with other external sound sources at varying levels. Therefore, we use six randomly selected samples from different background noises including traffic, people speaking, airport, etc.