Learning Self-Supervised Representations from Vision and Touch for Active Sliding Perception of Deformable Surfaces

Humans make extensive use of vision and touch as complementary senses, with vision providing global information about the scene and touch measuring local information during manipulation without suffering from occlusions. In this work, we propose a novel framework for learning multi-task visuo-tactile representations in a self-supervised manner. We design a mechanism which enables a robot to autonomously collect spatially aligned visual and tactile data, a key property for downstream tasks. We then train visual and tactile encoders to embed these paired sensory inputs into a shared latent space using cross-modal contrastive loss. The learned representations are evaluated without fine-tuning on 5 perception and control tasks involving deformable surfaces: tactile classification, contact localization, anomaly detection (e.g., surgical phantom tumor palpation), tactile search from a visual query (e.g., garment feature localization under occlusion), and tactile servoing along cloth edges and cables. The learned representations achieve an 80% success rate on towel feature classification, a 73% average success rate on anomaly detection in surgical materials, a 100% average success rate on vision-guided tactile search, and 87.8% average servo distance along cables and garment seams. These results suggest the flexibility of the learned representations and pose a step toward task-agnostic visuo-tactile representation learning for robot control.