This thesis introduces Armando, a low-cost soft robotic gripper with proprioceptive sensing using a single flexible capacitive sensor and neural-network decoding. Achieving 99% accuracy, Armando enables precise finger-position estimation for applications in prosthetics, assistive care, and disaster response, advancing accessible tactile robotics inspired by human touch.

Labour shortages leave millions of dollars of crops unharvested. This research develops touch-sensitive robots that navigate complex plants using force sensors rather than vision, reducing damage and improving fruit-reaching success by 66%. By learning from human movements, these robots could support sustainable agriculture and address critical workforce gaps.