Jungpyo Lee

Spinal cord injuries (SCI) substantially affect sensory, motor, and autonomous functions below the level of injury, reducing the independence and quality of life for affected individuals. Specifically, people with SCI between C5 and C7 cervical levels encounter limitations in voluntary finger and wrist flexion, reducing grasp capability. Compensatory strategies like tenodesis grasp, whereby wrist extension passively closes the fingers, remain; this is effective for small and light objects but insufficient for heavier ones. Typically, wearable assistive exoskeletons are designed to actuate a person’s fingers, however, such devices are sensitive to anatomical variability, such as hand size and joint contractures. The Dorsal Grasper is a wearable device designed to address this challenge by leveraging voluntary wrist extension and providing human-robot collaborative grasping capabilities with underactuated supernumerary fingers on the back of the hand. In this study, we introduce kinematic assessment methods that we use to show how the Dorsal Grasper expands the graspable workspace and reduces trunk motion, especially in situations where the use of a wheelchair restricts the individual’s posture. Our functionally relevant experiments with multiple SCI participants demonstrate the Dorsal Grasper’s potential as a versatile assistive solution for enhancing grasping capability in individuals with distinct SCI profiles.

Suction cups are an important gripper type in industrial robot applications, and the prior literature focuses on using vision-based planners to improve grasping success in these tasks. Vision-based planners can fail due to adversarial objects or lose generalizability for unseen scenarios, without retraining learned algorithms. In this article, we propose haptic exploration to improve suction cup grasping when visual grasp planners fail. We present the smart suction cup, an end effector that utilizes internal flow measurements for tactile sensing. We show that model-based haptic search methods, guided by these flow measurements, improve grasping success by up to 2.5× as compared with using only a vision planner during a bin-picking task. In characterizing the smart suction cup on both geometric edges and curves, we find that flow rate can accurately predict the ideal motion direction even with large postural errors. The smart suction cup includes no electronics on the cup itself, such that the design is easy to fabricate and haptic exploration does not damage the sensor. This work motivates the use of suction cups with autonomous haptic search capabilities in especially adversarial scenarios.