An autonomous robotic assistant for drinking

Stroke and neurodegenerative diseases, among a range of other neurologic disorders, can cause chronic paralysis. Patients suffering from paralysis may remain unable to achieve even basic everyday tasks such as liquid intake. Currently, there is a great interest in developing robotic assistants controlled via brain-machine interfaces (BMIs) to restore the ability to perform such tasks. This paper describes an autonomous robotic assistant for liquid intake. The components of the system include autonomous online detection both of the cup to be grasped and of the mouth of the user. It plans motions of the robot arm under the constraints that the cup stays upright while moving towards the mouth and that the cup stays in direct contact with the user's mouth while the robot tilts it during the drinking phase. To achieve this, our system also includes a technique for online estimation of the location of the user's mouth even under partial occlusions by the cup or robot arm. We tested our system in a real environment and in a shared-control setting using frequency-specific modulations recorded by electroencephalography (EEG) from the brain of the user. Our experiments demonstrate that our BMI-controlled robotic system enables a reliable liquid intake. We believe that our approach can easily be extended to other useful tasks including food intake and object manipulation.