UniTac-NV: 非視覚型触覚センサのための統合触覚表現
UniTac-NV: A Unified Tactile Representation For Non-Vision-Based Tactile Sensors
視覚ベース以外の触覚センサ間でデータを統合するエンコーダ・デコーダ型フレームワークを提案し、XelaとContactileのセンサ間で潜在空間を共有して接触形状推定を再学習なしで転移できることを示した。
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著者: Jian Hou, Xin Zhou, Qihan Yang, Adam J. Spiers
分類: cs.RO
原文アブストラクト
Generalizable algorithms for tactile sensing remain underexplored, primarily due to the diversity of sensor modalities. Recently, many methods for cross-sensor transfer between optical (vision-based) tactile sensors have been investigated, yet little work focus on non-optical tactile sensors. To address this gap, we propose an encoder-decoder architecture to unify tactile data across non-vision-based sensors. By leveraging sensor-specific encoders, the framework creates a latent space that is sensor-agnostic, enabling cross-sensor data transfer with low errors and direct use in downstream applications. We leverage this network to unify tactile data from two commercial tactile sensors: the Xela uSkin uSPa 46 and the Contactile PapillArray. Both were mounted on a UR5e robotic arm, performing force-controlled pressing sequences against distinct object shapes (circular, square, and hexagonal prisms) and two materials (rigid PLA and flexible TPU). Another more complex unseen object was also included to investigate the model's generalization capabilities. We show that alignment in latent space can be implicitly learned from joint autoencoder training with matching contacts collected via different sensors. We further demonstrate the practical utility of our approach through contact geometry estimation, where downstream models trained on one sensor's latent representation can be directly applied to another without retraining.
