To this end, we study:

• Neuromuscular biomechanics of human grasp to elucidate patterns of reflex-like grip responses that can be used as inspiration for low-level reflexes in artificial hands
• Neuromuscular control of multiple digits during manipulation tasks
• Tactile sensor technology that can provide rich tactile feedback for use in real-time control of artificial fingertips
• Machine-learning algorithms that will enable the mapping of tactile sensor signals to features of finger-object interactions
• Control and sensory challenges for human-machine systems (e.g., brain-controlled neuroprosthesis)
• Reducing cognitive burden via sensory-event driven, low-level reflex algorithms. Such artificial reflexes could serve as “survival instincts” which buy time for human operators of robotic devices to detect, process, and command a response to perturbations.

Example applications include:

• Prosthetic hands for improving the independence and quality of life of amputees
• Wheelchair-mounted robot hands for increasing the workspace, independence, and quality of life of wheelchair-bound individuals
• Autonomous or teleoperated manipulators for use in harsh or limited-access environments