Monolithic Integration of Neuromorphic Electronic Skin and In-Memory Computing Chips for Fine Manipulation in Embodied Intelligent Agents
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Abstract
For fine manipulation in embodied intelligent agents, tactile systems are required not only to provide highly sensitive perception, but also to support low-latency information processing and highly integrated hardware implementation. To address the large data-movement overhead, delayed response, and limited compactness caused by the separation of sensing, transmission, and computation in conventional tactile architectures, this work proposes a monolithically integrated platform that combines neuromorphic electronic skin with an in-memory computing chip to establish an efficient task-oriented tactile information processing pathway. Within a unified hardware architecture, the platform couples mechanical stimulus perception, neuromorphic encoding, and on-chip computing, enabling external tactile inputs to be dynamically represented at the sensory source and further processed for feature extraction and pattern discrimination in the in-memory computing array. The structural design, operating principle, and electrical, neuromorphic, and computing characteristics of the integrated platform are systematically investigated. In addition, representative fine-manipulation-related tasks, including contact-state recognition, slip warning, and texture classification, are employed to validate the system. The results demonstrate that the proposed platform effectively preserves the temporal information embedded in tactile events and outperforms conventional discrete architectures in terms of task-processing accuracy, real-time capability, and data-movement efficiency. This study provides a promising route toward highly integrated, low-latency, and task-driven tactile intelligence hardware, and lays the groundwork for the co-design of tactile sensing and on-chip processing in next-generation embodied intelligent systems.
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References
D. Lee, “Design consideration of autonomous robots based on embodied intelligence,” Clinical Research and Clinical Trials, vol. 12, no. 3, 2025.
R. Akerkar, “Robots with a Sense of Self: Exploring Embodied Intelligence,” in Artificial Intelligence: Transcending Traditional Paradigms. Springer, 2026, pp. 417–444.
S. Gao, Y. Dai, and A. Nathan, “Tactile and vision perception for intelligent humanoids,” Advanced Intelligent Systems, vol. 4, no. 2, Art. no. 2100074, 2022.
J. Zhao, “Contact-aware and multi-modal robotic manipulation,” dissertation, Massachusetts Institute of Technology, 2025.
Y. Sun, S. Zhang, Z. Chen, et al., “Soft contact simulation and manipulation learning of deformable objects with vision-based tactile sensor,” IEEE Transactions on Automation Science and Engineering, 2025.
Z. Zhang, B. Li, S. Qiu, et al., “Establishing the tactile ability of dexterous hands: a review of research on tactile acquisition, feedback, and perception,” International Journal of Intelligent Robotics and Applications, pp. 1–22, 2026.
L. Zou, C. Ge, Z. J. Wang, et al., “Novel tactile sensor technology and smart tactile sensing systems: A review,” Sensors, vol. 17, no. 11, Art. no. 2653, 2017.
Q. Li, O. Kroemer, Z. Su, et al., “A review of tactile information: Perception and action through touch,” IEEE Transactions on Robotics, vol. 36, no. 6, pp. 1619–1634, 2020.
C. S. Patil, S. B. Ghode, J. Kim, et al., “Neuromorphic devices for electronic skin applications,” Materials Horizons, vol. 12, no. 7, pp. 2045– 2088, 2025.
J. Sun, C. Zhang, C. Yang, et al., “Human skin-inspired neuromorphic sensors,” Soft Science, vol. 5, no. 2, 2025.
H. Jang, J. Lee, C. J. Beak, et al., “Flexible neuromorphic electronics for wearable near-sensor and in-sensor computing systems,” Advanced Materials, vol. 37, no. 9, Art. no. 2416073, 2025.
H. Zheng, K. J. Bai, and Y. Yi, “Enabling a new methodology of neural coding: Multiplexing temporal encoding in neuromorphic computing,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 31, no. 3, pp. 331–342, 2023.
S. Ma, J. Pei, W. Zhang, et al., “Neuromorphic computing chip with spatiotemporal elasticity for multi-intelligent-tasking robots,” Science Robotics, vol. 7, no. 67, Art. no. eabk2948, 2022.
W. Haensch, A. Raghunathan, K. Roy, et al., “Compute in-memory with non-volatile elements for neural networks: a review from a co-design perspective,” Advanced Materials, vol. 35, no. 37, Art. no. 2204944, 2023.
M. Rasouli, Y. Chen, A. Basu, et al., “An extreme learning machine-based neuromorphic tactile sensing system for texture recognition,” IEEE transactions on biomedical circuits and systems, vol. 12, no. 2, pp. 313– 325, 2018.