{"ID":2874790,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.04658","arxiv_id":"2509.04658","title":"Surformer v2: A Multimodal Classifier for Surface Understanding from Touch and Vision","abstract":"Multimodal surface material classification plays a critical role in advancing tactile perception for robotic manipulation and interaction. In this paper, we present Surformer v2, an enhanced multi-modal classification architecture designed to integrate visual and tactile sensory streams through a late(decision level) fusion mechanism. Building on our earlier Surformer v1 framework [1], which employed handcrafted feature extraction followed by mid-level fusion architecture with multi-head cross-attention layers, Surformer v2 integrates the feature extraction process within the model itself and shifts to late fusion. The vision branch leverages a CNN-based classifier(Efficient V-Net), while the tactile branch employs an encoder-only transformer model, allowing each modality to extract modality-specific features optimized for classification. Rather than merging feature maps, the model performs decision-level fusion by combining the output logits using a learnable weighted sum, enabling adaptive emphasis on each modality depending on data context and training dynamics. We evaluate Surformer v2 on the Touch and Go dataset [2], a multi-modal benchmark comprising surface images and corresponding tactile sensor readings. Our results demonstrate that Surformer v2 performs well, maintaining competitive inference speed, suitable for real-time robotic applications. These findings underscore the effectiveness of decision-level fusion and transformer-based tactile modeling for enhancing surface understanding in multi-modal robotic perception.","short_abstract":"Multimodal surface material classification plays a critical role in advancing tactile perception for robotic manipulation and interaction. In this paper, we present Surformer v2, an enhanced multi-modal classification architecture designed to integrate visual and tactile sensory streams through a late(decision level) f...","url_abs":"https://arxiv.org/abs/2509.04658","url_pdf":"https://arxiv.org/pdf/2509.04658v1","authors":"[\"Manish Kansana\",\"Sindhuja Penchala\",\"Shahram Rahimi\",\"Noorbakhsh Amiri Golilarz\"]","published":"2025-09-04T21:05:33Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Transformer\",\"Convolutional Neural Network\"]","has_code":false}