Multi-Task Sensorization of Soft Actuators Using Prior Knowledge

Abstract

The space of all possible deformations of soft robotic actuators is extremely large. It is impossible to explicitly measure each internal degree of freedom, regardless of the number and types of sensors. It is, however, possible to measure a smaller subset of task-relevant deformations using only a few well-placed sensors. But for a different task, the soft actuator's deformation behavior might differ significantly. Instead of finding a new sensor placement for the new task, which would result in a separate hand for every task, we propose a method that maintains the original sensors and uses prior knowledge about each task to extend the applicability of the existing sensorized actuators to new tasks. We demonstrate our approach by the example of a PneuFlex actuator of the RBO Hand 2. When sensorizing the actuator for a single task, the sensor model does not transfer well to other tasks. Using our multi-task method, we train new sensor models that use prior knowledge about the tasks. The new models improve measurement accuracy for the new tasks without having to change the sensor hardware.