Abstract
Meal assistant robots form a very important part of the assistive robotics sector since self-feeding is a priority activity of daily living (ADL) for people suffering from physical disabilities like tetraplegia. A quick survey of the current trends in this domain reveals that, while tremendous progress has been made in the development of assistive robots for the feeding of solid foods, the task of feeding liquids from a cup remains largely underdeveloped. Therefore, this paper describes an assistive robot that focuses specifically on the feeding of liquids from a cup using tactile feedback through force sensors with direct human–robot interaction (HRI). The main focus of this paper is the application of reinforcement learning (RL) to learn what the best robotic actions are, based on the force applied by the user. A model of the application environment is developed based on the Markov decision process and a software training procedure is designed for quick development and testing. Five of the commonly used RL algorithms are investigated, with the intention of finding the best fit for training, and the system is tested in an experimental study. The preliminary results show a high degree of acceptance by the participants. Feedback from the users indicates that the assistive robot functions intuitively and effectively.
Highlights
In recent years, reinforcement learning (RL) has been proposed as a solution to create individualized and customized human–robot interaction (HRI)
Assistive robotic manipulators have been under great interest in research, as well as the commercial market, to assist people suffering from severe motor impairments to perform activities of daily living (ADLs)
The review study conducted by Naotunna et al [4] outlines how the population of people suffering from impairments is increasing
Summary
Reinforcement learning (RL) has been proposed as a solution to create individualized and customized human–robot interaction (HRI). Interactive robots that adapt their behavior to the user’s needs are used in different applications such as social robots [1,2] and robot-assisted therapy [3]. There is limited work to address HRI applications which require direct contact. Assistive robotic manipulators have been under great interest in research, as well as the commercial market, to assist people suffering from severe motor impairments to perform activities of daily living (ADLs). Hall et al [5] report that there is good acceptance for assistive robotics in healthcare and ADLs. Drinking and eating assistive robotic tasks are considered highly prioritized [6] and require the robot to be in direct contact with the user. There is a clear and present need for economical and intuitive meal assistant robots (MAR). The number of steps to completely drink water was appropriate
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