Low-Dimensional Tactile Glove for Visuo-Tactile Robot Hand Control: A Preliminary Study

Abstract

Dexterous control of multi-joint manipulators such as humanoid robot hands remains challenging when relying solely on visual feedback. Cameras are fundamentally limited in measuring contact forces, slip, and surface deformation during physical interaction, and are susceptible to occlusion in contact-rich scenarios. Tactile sensing is therefore widely considered essential for robust dexterous manipulation. However, existing approaches predominantly rely on high-cost sensors, imposing non-trivial burdens on data collection and robot deployment, which constrains the scalability of tactile sensing in practical robotic systems. To address these limitations, we present a low-dimensional wearable tactile glove as a scalable platform for visuo-tactile robot hand control, and propose a two-level learning framework built upon it. The glove incorporates 20 FSR400 sensors and achieves stable 300 Hz acquisition through dual multiplexing and WiFi TCP communication with clock synchronization. Hardware validation confirms low inter-frame jitter and noise-free signal acquisition across all channels. The proposed framework first investigates whether binary tactile signals are sufficient to recover meaningful force distributions through learning, and subsequently extends to visuo-tactile representation learning, where tactile and visual modalities are jointly leveraged to learn shared cross-modal representations for downstream manipulation tasks.