Touch and slippage detection in robotic hands with spiking neural networks

Significant research efforts have focused on advancing upper limb prostheses, addressing issues such as dexterity, embodiment, weight, and resistance. These key features require enhancements to mitigate prosthesis abandonment. Sensory feedback is a vital feature for improving the dexterity and embodiment of prostheses for patients. Current techniques utilize electronic signals for feedback to control systems and to partially restore tactile sensations to amputees. However, it has already been demonstrated that, in order to elicit responses from biological neurons signals must be converted into neuromorphic tactile information. Most of those demonstrations relied on algorithmic implementations of mechanoreceptor and neuron models running on classic processor cores. However, integrating real-time encoding and control strategies into such architectures is challenging due to computational complexity and high power usage. A prospective solution is to increasingly incorporate neuromorphic technologies for prosthesis sensing and control. In this work, we demonstrate the feasibility of using spiking neural networks for performing two key functions in controlling upper limb prosthesis: touch and slippage detection. We found that a simple two-layered architecture with a few tens of neurons is sufficient to identify slip and touch with nearly 100% accuracy. The first layer consists of a heterogeneous population of neurons with their parameters adjusted to model slowly and fast-adapting type 1 mechanoreceptors. An output layer classifies touch or slip events. Furthermore, simulations showed that implementing the network in ultra-low-power neuromorphic hardware, such as Loihi, has the potential to reduce power consumption by up to 162 times, with respect to a von Neumann processor.