Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands.
Microsystems, Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Nat Commun. 2024 Jun 4;15(1):4765. doi: 10.1038/s41467-024-48881-2.
Biological systems interact directly with the environment and learn by receiving multimodal feedback via sensory stimuli that shape the formation of internal neuronal representations. Drawing inspiration from biological concepts such as exploration and sensory processing that eventually lead to behavioral conditioning, we present a robotic system handling objects through multimodal learning. A small-scale organic neuromorphic circuit locally integrates and adaptively processes multimodal sensory stimuli, enabling the robot to interact intelligently with its surroundings. The real-time handling of sensory stimuli via low-voltage organic neuromorphic devices with synaptic functionality forms multimodal associative connections that lead to behavioral conditioning, and thus the robot learns to avoid potentially dangerous objects. This work demonstrates that adaptive neuro-inspired circuitry with multifunctional organic materials, can accommodate locally efficient bio-inspired learning for advancing intelligent robotics.
生物系统直接与环境相互作用，并通过接收多模态反馈来学习，这些反馈通过感觉刺激来塑造内部神经元表示的形成。受探索和感觉处理等生物概念的启发，这些概念最终导致行为条件作用，我们提出了一个通过多模态学习来处理物体的机器人系统。一个小规模的有机神经形态电路在本地集成和自适应地处理多模态感觉刺激，使机器人能够与周围环境进行智能交互。通过具有突触功能的低压有机神经形态设备实时处理感觉刺激，形成多模态联想连接，从而导致行为条件作用，机器人学会避免潜在危险的物体。这项工作表明，具有多功能有机材料的自适应神经启发电路可以适应本地高效的生物启发学习，从而推动智能机器人技术的发展。
