Reinforcement Learning-Based Assistive Framework for Disabled Persons

Robot programming by demonstration (PbD) enables human users to teach the robot and increases its capabilities by interactive teaching without having to manually program the robot. PbD combined with intelligent machine learning algothrims can help us to develop autonomous robots for various industrial and domestic tasks. One such task is the pouring of liquids from bottle into the cup/glass. In this paper the first step is to teach the robot liquid pouring task by the human user in order to facilitate physically disabled people in making various types of drinks and then dataset is created from the user taken demonstrations. In training stage the dataset obtained is feed to the decision-making algorithm based on reinforcement learning. The algorithm enables the robot to learn to pour different liquids under different pouring conditions with the help of minimum number of human user demonstrations needed for the learning of task. The acquired results show that the robot can learn to pour different liquids and is able to accurately adapt to different pouring conditions by using reward-based system and online feedback. Furthermore the results show that the proposed framework can work with different types of liquid and container sizes without any further need for reprogramming or demonstrations.