Training Tactile Sensors to Learn Force Sensing from Each Other

Humans achieve stable and dexterous object manipulation by coordinating grasp forces across multiple fingers and palms, facilitated by a unified tactile memory system in the somatosensory cortex. This system encodes and stores tactile experiences across skin regions, enabling the flexible reuse and transfer of touch information between fingers and hands. Inspired by this biological capability, we present GenForce, a framework that enables transferable force sensing across tactile sensors in robotic hands. GenForce unifies tactile signals into shared marker representations, analogous to cortical sensory encoding, allowing force prediction models trained on one sensor to be transferred to others without the need for exhaustive force data collection. We demonstrate that GenForce generalizes across both homogeneous sensors with varying configurations and heterogeneous sensors with distinct sensing modalities and material properties. Our results highlight a scalable paradigm for robotic tactile learning, offering new pathways toward adaptable and tactile memory-driven manipulation in unstructured environments.