This study proposes an object identification method that combines high-precision tracing motion by an industrial robot with vibration information obtained from a polyvinylidene fluoride (PVDF) film sensor mounted on the finger surface. In conventional tactile sensing technology, methods that place sensors on the contact surface face challenges such as reduced durability due to wear and limited design freedom for the finger surface. The proposed method overcomes these issues by exploiting vibration propagation to physically isolate the sensor from the contact point. In this paper, we first investigate how tracing speed and pressing depth affect the vibration spectrum based on the mechanism of vibration generation. We then verify, through fundamental experiments using sandpaper, how finger material influences vibration transmission characteristics. The results demonstrate that a lightweight resin finger is advantageous for acquiring high-frequency vibrations and that strict control of tracing speed is essential for ensuring identification accuracy. Based on these findings, we conducted identification experiments using wide-band vibration information from 0 Hz to 8,000 Hz as features. The experiments targeted 33 types of diverse objects selected from real-world environments according to onomatopoeic classification. Evaluation with a support vector machine achieved an extremely high identification rate exceeding 99% for both finely textured sandpaper and real-world objects under high-precision robot control. Furthermore, high identification rates were maintained even when the tracing position and pressing depth were varied randomly, demonstrating the effectiveness and robustness of the proposed method.

1. [1] S. Hido, “Artificial Intelligence Technology for Industrial Robot Applications,” J. of the Robotics Society of Japan, Vol.35, No.3, pp. 186-190, 2017 (in Japanese). https://doi.org/10.7210/jrsj.35.186
2. [2] Q. Li, O. Kroemer, Z. Su, F. F. Veiga, M. Kaboli, and H. J. Ritter, “A Review of Tactile Information: Perception and Action Through Touch,” IEEE Trans. on Robotics, Vol.36, No.6, pp. 1619-1634, 2020. https://doi.org/10.1109/TRO.2020.3003230
3. [3] J. J. Gibson, “The Senses Considered as Perceptual Systems,” Houghton Mifflin, 1966.
4. [4] S. J. Lederman and R. L. Klatzky, “Hand movements: A window into haptic object recognition,” Cognitive Psychology, Vol.19, No.3, pp. 342-368, 1987. https://doi.org/10.1016/0010-0285(87)90008-9
5. [5] M. Hollins and S. R. Risner, “Evidence for the duplex theory of tactile texture perception,” Perception & Psychophysics, Vol.62, No.4, pp. 695-705, 2000. https://doi.org/10.3758/BF03206916
6. [6] S. J. Bensmaïa and M. Hollins, “The vibrations of texture,” Somatosensory & Motor Research, Vol.20, No.1, pp. 33-43, 2003. https://doi.org/10.1080/0899022031000083825
7. [7] H. Lee, K. Park, Y. Kim, and J. Kim, “Durable and repairable soft tactile skin for physical human robot interaction,” Proc. of the 2017 ACM/IEEE Int. Conf. on Human-Robot Interaction, pp. 183-184, 2017. https://doi.org/10.1145/3029798.3038417
8. [8] Z. Kappassov, J.-A. Corrales, and V. Perdezuela, “Tactile sensing in dexterous robot hands – Review,” Robotics and Autonomous Systems, Vol.74, Part A, pp. 195-220, 2015. https://doi.org/10.1016/j.robot.2015.07.015
9. [9] A. Khasnobish, G. Singh, A. Jati, A. Konar, and D. N. Tibarewala, “Object-shape recognition and 3D reconstruction from tactile sensor images,” Medical & Biological Engineering & Computing, Vol.52, No.4, pp. 353-362, 2014. https://doi.org/10.1007/s11517-014-1142-1
10. [10] W. Yuan, S. Dong, and E. H. Adelson, “GelSight: High-Resolution Robot Tactile Sensors for Estimating Geometry and Force,” Sensors, Vol.17, No.12, Article No.2762, 2017. https://doi.org/10.3390/s17122762
11. [11] N. Jamali and C. Sammut, “Material classification by tactile sensing using surface textures,” 2010 IEEE Int. Conf. on Robotics and Automation, pp. 2336-2341, 2010. https://doi.org/10.1109/ROBOT.2010.5509675
12. [12] J. A. Fishel and G. E. Loeb, “Bayesian exploration for intelligent identification of textures,” Frontiers in Neurorobotics, Vol.6, Article No.4, 2012. https://doi.org/10.3389/fnbot.2012.00004
13. [13] Y. Xin, H. Tian, C. Guo, X. Li, H. Sun, P. Wang, J. Lin, S. Wang, and C. Wang, “PVDF tactile sensors for detecting contact force and slip: A review,” Ferroelectrics, Vol.504, No.1, pp. 31-45, 2016. https://doi.org/10.1080/00150193.2016.1238723
14. [14] J. Dargahi and S. Najarian, “A supported membrane type sensor for medical tactile mapping,” Sensor Review, Vol.24, No.3, pp. 284-297, 2004. https://doi.org/10.1108/02602280410545416
15. [15] R. Kikuuwe, K. Nakamura, and M. Yamamoto, “Finger-Mounted Tactile Sensor for Evaluating Surfaces,” J. Robot. Mechatron., Vol.24, No.3, pp. 430-440, 2012. https://doi.org/10.20965/jrm.2012.p0430
16. [16] H. Hu, Y. Han, A. Song, S. Chen, C. Wang, and Z. Wang, “A Finger-Shaped Tactile Sensor for Fabric Surfaces Evaluation by 2-Dimensional Active Sliding Touch,” Sensors, Vol.14, No.3, pp. 4899-4913, 2014. https://doi.org/10.3390/s140304899
17. [17] P. Yu, W. Liu, C. Gu, X. Cheng, and X. Fu, “Flexible Piezoelectric Tactile Sensor Array for Dynamic Three-Axis Force Measurement,” Sensors, Vol.16, No.6, Article No.819, 2016. https://doi.org/10.3390/s16060819
18. [18] S. Luo, J. Bimbo, R. Dahiya, and H. Liu, “Robotic tactile perception of object properties: A review,” Mechatronics, Vol.48, pp. 54-67, 2017. https://doi.org/10.1016/j.mechatronics.2017.11.002
19. [19] J. M. Romano and K. J. Kuchenbecker, “Creating realistic virtual textures from contact acceleration data,” IEEE Trans. on Haptics, Vol.5, No.2, pp. 109-119, 2012. https://doi.org/10.1109/TOH.2011.38
20. [20] J. Sinapov, V. Sukhoy, R. Sahai, and A. Stoytchev, “Vibrotactile recognition and categorization of surfaces by a humanoid robot,” IEEE Trans. on Robotics, Vol.27, No.3, pp. 488-497, 2011. https://doi.org/10.1109/TRO.2011.2127130
21. [21] Y. Tanaka, D. P. Nguyen, T. Fukuda, and A. Sano, “Wearable skin vibration sensor using a PVDF film,” 2015 IEEE World Haptics Conf. (WHC), pp. 146-151, 2015. https://doi.org/10.1109/WHC.2015.7177705
22. [22] C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, Vol.20, pp. 273-297, 1995. https://doi.org/10.1007/BF00994018
23. [23] C. J. C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery, Vol.2, pp. 121-167, 1998. https://doi.org/10.1023/A:1009715923555
24. [24] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. of Machine Learning Research, Vol.12, No.85, pp. 2825-2830, 2011.
25. [25] J. Scheibert, S. Leurent, A. Prevost, and G. Debrégeas, “The role of fingerprints in the coding of tactile information probed with a biomimetic sensor,” Science, Vol.323, No.5920, pp. 1503-1506, 2009. https://doi.org/10.1126/science.1166467
26. [26] T. Hayakawa, S. Matsui, and J. Watanabe, “Classification method of tactile textures using onomatopoeias,” Trans. of the Virtual Reality Society of Japan, Vol.15, No.3, pp. 487-490, 2010 (in Japanese). https://doi.org/10.18974/tvrsj.15.3_487
27. [27] K. O. Johnson and J. R. Phillips, “Tactile spatial resolution. I. Two-point discrimination, gap detection, grating resolution, and letter recognition,” J. of Neurophysiology, Vol.46, No.6, pp. 1177-1192, 1981. https://doi.org/10.1152/jn.1981.46.6.1177
28. [28] M. Kaboli, K. Yao, D. Feng, and G. Cheng, “Tactile-based active object discrimination and target object search in an unknown workspace,” Autonomous Robots, Vol.43, pp. 123-152, 2019. https://doi.org/10.1007/s10514-018-9707-8