Learning generalizable behaviors from demonstration.

Generalizing prior experiences to complete new tasks is a challenging and unsolved problem in robotics. In this work, we explore a novel framework for control of complex systems called Primitive Imitation for Control (). The approach combines ideas from imitation learning, task decomposition, and novel task sequencing to generalize from demonstrations to new behaviors.

Eye Movement Control in the Argus II Retinal-Prosthesis Enables Reduced Head Movement and Better Localization Precision.

Purpose: Visual scanning by sighted individuals is done using eye and head movements. In contrast, scanning using the Argus II is solely done by head movement, since eye movements can introduce localization errors. Here, we tested if a scanning mode utilizing eye movements increases visual stability and reduces head movements in Argus II users.

Eye movements as a marker for visual prosthesis spatial mapping - A feasibility study using a blind patient implanted with the Argus II retinal prosthesis.

Spatial mapping, the location in space of a perceived location due to an implanted electrode's electrical stimulation is important in the design of visual prostheses. Generally, a visual prosthesis system consists of an implanted electrode array, an external camera that acquires the image, and a transmitter that sends the information to the implanted electrodes. In cortical visual implant, the layout of the implanted array in most cases does not match the retinotopic map and it is necessary to find the location of the percept of each electrode in world coordinates.

Embedded clutter reduction and face detection algorithms for a visual prosthesis.

Retinal prosthetic devices can significantly and positively impact the ability of visually challenged individuals to live a more independent life. We describe a visual processing system which leverages image analysis techniques to produce visual patterns and allows the user to more effectively perceive their environment. These patterns are used to stimulate a retinal prosthesis to allow self guidance and a higher degree of autonomy for the affected individual.

Individual finger control of a modular prosthetic limb using high-density electrocorticography in a human subject.

Objective: We used native sensorimotor representations of fingers in a brain-machine interface (BMI) to achieve immediate online control of individual prosthetic fingers.

Approach: Using high gamma responses recorded with a high-density electrocorticography (ECoG) array, we rapidly mapped the functional anatomy of cued finger movements. We used these cortical maps to select ECoG electrodes for a hierarchical linear discriminant analysis classification scheme to predict: (1) if any finger was moving, and, if so, (2) which digit was moving.