We investigate next‑generation adaptive distributed autonomous systems through the lens of anthropomorphic prosthetics, androids, and intelligent robotic platforms. Our research integrates cutting‑edge neuroscience, artificial intelligence, neuromorphic computing, and robotics to create highly responsive, lifelike systems capable of operating autonomously while adapting to human intent and dynamic environments. Leveraging neuromorphic architectures and spiking neural networks, we develop control frameworks that enable natural, intuitive interaction between artificial limbs, androids, and biological systems. These brain‑inspired models support real‑time adaptation, low‑power operation, and seamless communication across distributed components. Our work on non‑invasive neural interfaces allows prosthetic devices to adjust continuously to user intent, improving precision, comfort, and fluidity of motion. In parallel, our research on advanced sensory processing equips androids with human‑like perceptual capabilities, enabling them to interpret complex environmental stimuli, collaborate with humans, and function autonomously within distributed multi‑agent settings.

N-HuRo is a next‑generation distributed platform of anthropomorphic humanoid robots designed to operate collaboratively across diverse and demanding environments. Built on advanced neuromorphic control architectures, the system enables human‑like motion, adaptive behavior, and emergent cognitive capabilities inspired by biological neural processes. The platform integrates neuro‑inspired perception, decision‑making, and motor control to achieve fluid, realistic interactions and robust autonomy. Its distributed design allows multiple humanoid units to coordinate seamlessly, share sensory information, and execute complex tasks that exceed the capabilities of a single robot. AIzuAnthor targets a wide spectrum of high‑stakes applications — from critical mission support in space exploration, to firefighting and disaster response, to field operations in hazardous or inaccessible environments. By combining anthropomorphic embodiment with neuromorphic intelligence, the project aims to establish a versatile, resilient, and human‑compatible robotic ecosystem for the next era of intelligent machines.

• Zhishang Wang, Yassine Mohamed Khedher, Khanh N. Dang, Michael Cohen and Abderazek Ben Abdallah, "Analytical Modeling of Task Allocation for Distributed Anthropomorphic Robots in Mission-Critical Environments," 2025 IEEE 18th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), Singapore, Dec. 15-18, 2025.
• Cheng Hong, Sinchhean Phea, Khanh N. Dang, Abderazek Ben Abdallah, "The AIzuHand Neuromorphic Prosthetic Hand," ETLTC2023, January 24-27, 2023.
• Yamato Saikawa, Khanh N. Dang, Abderazek Ben Abdallah, "Multimodal sEMG and Speech-Based Design and Evaluation of a Low-Cost," ETLTC2023, January 24-27, 2023.
• Mark Ogbodo, Abderazek Ben Abdallah, "Study of a Multi-modal Neurorobotic Prosthetic Arm Control System based on Recurrent Spiking Neural Network," ETLTC2022, January 25-28, 2022.
• Yamato Saikawa, Abderazek Ben Abdallah, "Study of Deep Learning-based Hand Gesture Recognition Toward the Design of a Low-cost Prosthetic Hand," ETLTC2022, January 25-28, 2022.
• Masaki Watanabe, Abderazek Ben Abdallah, "A Low-cost Raspberry Pi-based Control System for Upper Limb Prosthesis," ETLTC2022, January 25-28, 2022.
• Sinchhean Phea, Abderazek Ben Abdallah, "An Affordable 3D-printed Open-Loop Prosthetic Hand Prototype with Neural Network Learning EMG-Based Manipulation for Amputees," ETLTC2022, January 25-28, 2022.
• Sinchhean Phea, Zhishang Wang, Jiangkun Wang, Abderazek Ben Abdallah, ''Optimization and Implementation of a Collaborative Learning Algorithm for an AI-Enabled Real-time Biomedical System,'' 3rd ETLTC2021-ACM Chapter Int. Conference on Information and Comm. Technology, January 27-30, 2021, Aizu-Wakamatsu, Japan, SHS Web Conf., 102 (2021) 04017, DOI:10.1051/shsconf/202110204017 (Best Paper Award).

Related Patents:

• P14-2024-PAbderazek Ben Abdallah , Zhishang Wang, K. N. Dang, Masayuki Hisada, ''Self Controlled Urushi Painting System/自己制御漆ロボット手描きプラットフォームとシステム'', 特願2024-