Gao, Tian, T., Lin, Z., & Wu, Y. (2021). On Explainability and Sensor-Adaptability of a Robot Tactile Texture Representation Using a Two-Stage Recurrent Networks. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://doi.org/10.1109/iros51168.2021.9636380
Abstract:
The ability to simultaneously distinguish objects, materials, and their associated physical properties is one fundamental function of the sense of touch. Recent advances in the development of tactile sensors and machine learning techniques allow more accurate and complex modelling of robotic tactile sensations. However, many state-of-the-art (SotA) approaches focus solely on constructing black-box models to achieve ever higher classification accuracy and fail to adapt across sensors with unique spatial-temporal data formats. In this work, we propose an Explainable and Sensor-Adaptable Recurrent Networks (ExSARN) model for tactile texture representation. The ExSARN model consists of a two-stage recurrent networks fed by a sensor-specific header network. The first stage recurrent network emulates our human touch receptors and decouples sensor-specific tactile sensations into different frequency response bands, while the second stage codes the overall temporal signature as a variational recurrent autoen-coder. We infuse the latent representation with ternary labels to qualitatively represent texture properties (e.g. roughness and stiffness), which facilitates representation learning and provide explainability to the latent space. The ExSARN model is tested on texture datasets collected with two different tactile sensors. Our results show that the proposed model not only achieves higher accuracy, but also provides adaptability across sensors with different sampling frequencies and data formats. The addition of the crudely obtained qualitative property labels offers a practical approach to enhance the interpretability of the latent space, facilitate property inference on unseen materials, and improve the overall performance of the model.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - AME Programmatic
Grant Reference no. : A18A2b0046