Despite significant progress in the realm of upper-limb prosthetic design, end-users still abandon modern myoelectric prostheses, with haptic feedback listed as a primary need. The passive scheme of cable-driven body-powered prostheses provides kinesthetic sensory information to the user but can also lead to discomfort and fatigue due to the large loads applied to the body during operation. In order to investigate the role of this kinesthetic feedback on grasp force control, we design a body-powered prosthesis emulator capable of varying the amount of displayed force feedback along a continuous scale. Using this experimental test bed, we collect data from 9 participants while they perform a grasp and lift task. Analysis shows that, with increasing amounts of force feedback, people produce lower and steadier grasp forces but also become more prone to dropping held objects. These results suggest that the use of moderate amounts of feedback provides significant grasp performance benefits while also mitigating some of the shortcomings of cable-driven prostheses. These findings support the continued study of the incorporation of kinesthetic feedback into novel prosthetic designs.

The ocean is a harsh and unstructured environment for robotic systems; high ambient pressures, saltwater corrosion and low-light conditions demand machines with robust electrical and mechanical parts that are able to sense and respond to the environment. Prior work shows that the addition of gentle suction flow to the hands of underwater robots can aid in the handling of objects during mobile manipulation tasks. The current paper explores using this suction flow mechanism as a new modality for tactile sensing; by monitoring orifice occlusion we can get a sense of how objects make contact in the hand. The electronics required for this sensor can be located remotely from the hand and the signal is insensitive to large changes in ambient pressure associated with diving depth. In this study, suction is applied to the fingertips of a two-fingered compliant gripper and suction-based tactile sensing is monitored while an object is pulled out of a pinch grasp. As a proof of concept, a recurrent neural network model was trained to predict external force trends using only the suction signals. This tactile sensing modality holds the potential to enable automated robotic behaviors or to provide operators of remotely operated vehicles with additional feedback in a robust fashion suitable for ocean deployment.