Robot-assisted feeding: A technical application that combines learning from demonstration and visual interaction

Abstract

BACKGROUND:

The traditional meal assistance robots use human-computer interaction such as buttons, voice, and EEG. However, most of them rely on excellent programming technology for development, in parallelism with exhibiting inconvenient interaction or unsatisfactory recognition rates in most cases.

OBJECTIVE:

To develop a convenient human-computer interaction mode with a high recognition rate, which allows users to make the robot show excellent adaptability in the new environment without programming ability.

METHODS:

A visual interaction method based on deep learning was used to develop the feeding robot: when the camera detects that the user’s mouth is open for 2 seconds, the feeding command is turned on, and the feeding is temporarily conducted when the eyes are closed for 2 seconds. A programming method of learning from the demonstration, which is simple and has strong adaptability to different environments, was employed to generate a feeding trajectory.

RESULTS:

The user is able to eat independently through convenient visual interaction, and it only requires the caregiver to drag and teach the robotic arm once in the face of a new eating environment.