Soft robots are intrinsically capable of adapting to different environments by changing their shape in response to interaction forces. However, sensory feedback is still required for higher level decisions. Most sensing technologies integrate separate sensing elements in soft actuators, which presents a considerable challenge for both the fabrication and robustness of soft robots. Here we present a versatile sensing strategy that can be retrofitted to existing soft fluidic devices without the need for design changes. We achieve this by measuring the fluidic input that is required to activate a soft actuator during interaction with the environment, and relating this input to its deformed state. We demonstrate the versatility of our strategy by tactile sensing of the size, shape, surface roughness and stiffness of objects. We furthermore retrofit sensing to a range of existing pneumatic soft actuators and grippers. Finally, we show the robustness of our fluidic sensing strategy in closed-loop control of a soft gripper for sorting, fruit picking and ripeness detection. We conclude that as long as the interaction of the actuator with the environment results in a shape change of the interval volume, soft fluidic actuators require no embedded sensors and design modifications to implement useful sensing.

Using GPS data of 370 long-haul trips in India, survey data of 49 truck drivers, and ERP data, this study examines the role of driver personality characteristics in predicting risky and productive driving. The results show that more conscientious drivers display more risky driving behavior. More extravert drivers are less productive, whereas driver safety consciousness positively relates to productivity. These results can serve as a starting point for further studies into how long-haul transport companies may use individual truck driver characteristics in their training and selection procedures to meet operational safety and productivity objectives.