Visual-Haptic-Kinesthetic Object Recognition with Multimodal Transformer

Humans recognize objects by combining multi-sensory information in a coordinated fashion. However, visual-based and haptic-based object recognition remain two separate research directions in robotics. Visual images and haptic time series have different properties, which can be difficult for robots to fuse for object recognition as humans do. In this work, we propose an architecture to fuse visual, haptic and kinesthetic data for object recognition, based on the multimodal Convolutional Recurrent Neural Networks with Transformer. We use Convolutional Neural Networks (CNNs) to learn spatial representation, Recurrent Neural Networks (RNNs) to model temporal relationships, and Transformer's self-attention and cross-attention structures to focus on global and cross-modal information. We propose two fusion methods and conduct experiments on the multimodal AU dataset. The results show that our model offers higher accuracy than the latest multimodal object recognition methods. We conduct an ablation study on the individual components of the inputs to demonstrate the importance of multimodal information in object recognition. The codes will be available at https://github.com/SYLan2019/VHKOR.