Learning to Control Complex Rehabilitation Robot Using High-Dimensional Interfaces

Abstract

Upper body function is lost when injuries are sustained to the cervical spinal cord. Assistive machines can support the loss in upper body motor function. To regain functionality at the level of performing activities of daily living (e.g., self-feeding), though, assistive machines need to be able to operate in high dimensions. This means there is a need for interfaces with the capability to match high-dimensional operation. The body-machine interface provides this capability and has shown to be a suitable interface even for individuals with limited mobility. This is because it can take advantage of peoples available residual body movements. Previous studies using this interface have only shown that the interface can control low-dimensional assistive machines. In this pilot study, we demonstrate the interface can scale to high-dimensional robots, can be learned to control a 7-dimensional assistive robotic arm, to perform complex reaching and functional tasks, by an uninjured population. We also share results from various analyses that hint at learning, even when performance is extremely low. Decoupling intrinsic correlations between robot control dimensions seem to be a factor in learning--that is, proficiency in activating each control dimension independently may contribute to learning and skill acquisition of high-dimensional robot control. In addition, we show that learning to control the robot and learning to perform complex movement tasks can occur simultaneously.