触覚センサ間で力覚を相互学習させるフレームワークGenForce
Training Tactile Sensors to Learn Force Sensing from Each Other
触覚信号を共通のマーカー表現に統一することで、あるセンサで学習した力予測モデルを他のセンサへ転移可能にし、把持や滑り検出などの力制御を実現した研究。
著者: Zhuo Chen, Ni Ou, Xuyang Zhang, Zhiyuan Wu, Yongqiang Zhao, Yupeng Wang, Emmanouil Spyrakos Papastavridis, Nathan Lepora, Lorenzo Jamone, Jiankang Deng, Shan Luo
分類: cs.RO
原文アブストラクト
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. Inspired by this biological capability, we present GenForce, the first 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. This transferable force sensing is also demonstrated with high performance in robot force control including daily object grasping, slip detection and avoidance. Our results highlight a scalable paradigm for cross-sensor robotic tactile learning, offering new pathways toward adaptable and tactile memory-driven manipulation in unstructured environments.
