Fig. 1: Concept illustration of centralized and decentralized intelligence in robotics.

In the centralized approach, sensing elements are decoupled from the signal processing circuitry. All the learning happens at a powerful large central processor. In comparison, in the proposed decentralized approach, learning is embedded into the sensor nodes, reducing the wiring complexity at the same time improving latency and fault tolerance. In this work, pressure signals from mechanoreceptors are processed by small distributed intelligence units—each comprising a sensing element, satellite threshold adjusting receptors (STARs) that learn nociceptive or pain signals locally, satellite weight adjusting resistive memories (SWARMs) that learn texture signals locally and establish association between the texture and nociceptive signals, and satellite spiking neurons (SSNs) that integrate synaptic weights.