Efficient Visuo-Tactile Learning Via Fine-Grained Alignment and Importance-Aware Token Retention
Year: 2026
Type: conference-paper
Abstract: Visuo-tactile learning is crucial for robotic manipulation, yet current methods are limited by coarse-grained feature alignment and computational inefficiency. To address this, an efficient visuo-tactile learning framework with dual-level optimization is proposed for representation learning and inference. At the representation level, generalizable and fine-grained features are enhanced by combining dynamic mask-based token alignment with the alignment of physics-based salient tactile foregrounds to attribute-specific semantic text. At the inference level, an Importance-aware Token Retention mechanism is introduced that prunes non-salient tokens, reducing computational cost quadratically and enabling real-time application. Extensive experiments show performance comparable to or surpassing state-of-the-art accuracy across multiple downstream tasks while maintaining a balance between performance and efficiency.
Institutions: University of Glasgow, Tianjin University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funder: Royal Academy of Engineering
OpenAlex ID: W7155064237