Embodying soft robots with octopus-inspired hierarchical suction intelligence

Abstract

Octopuses exploit an efficient neuromuscular hierarchy to achieve complex dexterous body manipulation, integrating sensor-rich suckers, in-arm embodied computation, and centralized higher-level reasoning. Here, we take inspiration from the hierarchical intelligence of the octopus and demonstrate how, by exploiting the fluidic energy and information capacity of simple suction cups, soft computational elements, and soft actuators, we can mimic key aspects of the neuromuscular structure of the octopus in soft robotic systems. The presented suction intelligence works at two levels: By coupling suction flow with local fluidic circuitry, soft robots can achieve octopus-like low-level embodied intelligence, including gently grasping delicate objects, adaptive curling, and encapsulating objects of unknown geometries, and by decoding the pressure response from a suction cup, robots can achieve multimodal high-level perception, including contact detection, classification of an environmental medium and surface roughness, and prediction of an interactive pulling force. As in octopuses, suction intelligence executes most of its computation within lower-level local fluidic circuitries, and minimum information is transmitted to the high-level decision-making of the "brain." This development provides insights into octopus-inspired machine intelligence through low-cost, simple, and easy-to-integrate methods. The presented suction intelligence can be readily integrated into fluidic-driven soft robots to enhance their intelligence and reduce their computational requirement and can be applied widely, from industrial object handling and robotic manufacturing to robot-assisted harvesting and interventional health care.