Shared Control of Assistive Robots through User-intent Prediction and Hyperdimensional Recall of Reactive Behavior

Abstract

There is increasing interest in shared control for assistive robotics with adaptable levels of supervised autonomy. In this work, we present a user-adaptive multi-layer shared control scheme for control of assistive devices. The system leverages the advantages of brain-inspired hyperdimensional computing (HDC) for classification & recall of reactive robotic behavior including high performance, computational efficiency and intelligent sensor fusion, to execute actuation based on the user's goal while alleviating the burden of fine control. Using a multi-modal dataset of activities of daily living, we first recognize the user's most recent behaviors, then predict the user's next action based on their habitual action sequences, and finally, determine actuation through HDC recall-based shared control which intelligently deliberates between the predicted action and sensor feedback-based autonomy. In this work, we independently implement each layer to achieve >92% accuracy and then integrate the layers and discuss the combined performance and methods to reduce accumulated error.