A brain-inspired robot pain model based on a spiking neural network - Feng, Hui; Zeng, Yi (Frontiers in Neurorobotics, 2022)
Pain is a crucial function for organisms. Building a 'Robot Pain' model inspired by organisms' pain could help the robot learn self-preservation and extend longevity. Most previous studies about robots and pain focus on robots interacting with people by recognizing their pain expressions or scenes, or avoiding obstacles by recognizing dangerous objects. Robots do not have human-like pain capacity and cannot adaptively respond to danger. Inspired by the evolutionary mechanisms of pain emergence and the Free Energy Principle (FEP) in the brain, we summarize the neural mechanisms of pain and propose a Brain-inspired Robot Pain Spiking Neural Network (BRP-SNN) with spike-time-dependent-plasticity (STDP) learning rule and population coding method. The proposed model can quantify machine injury by detecting the coupling relationship between multi-modality sensory information and generate 'Robot Pain'. We provide a comparative analysis with the results of neuroscience experiments, showing that our model has more biological interpretability. We successfully tested our model on two tasks with real robots -- the alerting actual injury task and the preventing potential injury task, which has positive implications for constructing biologically plausible brain-inspired intelligent robots. In addition, our work provides a new insight to explore the essence of pain and has significant value for pain research in the cognitive neuroscience field in the future.