Learning-based control approaches for service robots on cloth manipulation and dressing assistance: a comprehensive review

BACKGROUND: Service robots are defined as reprogrammable, sensor-based mechatronic devices that perform useful services in an autonomous or semi-autonomous way to human activities in an everyday environment. As the number of elderly people grows, service robots, which can operate complex tasks like dressing tasks for disabled people, are being demanded increasingly. Consequently, there is a growing interest in studying dressing tasks, such as putting on a t-shirt, a hat, or shoes. Service robots or robot manipulators have been developed to accomplish these tasks using several control approaches. The robots used in this kind of application are usually bimanual manipulator (i.e. Baxter robot) or single manipulators (i.e. Ur5 robot). These arms are usually used for recognizing clothes and then folding them or putting an item on the arm or on the head of a person. METHODS: This work provides a comprehensive review of the most relevant attempts/works of robotic dressing assistance with a focus on the control methodology used for dressing tasks. Three main areas of control methods for dressing tasks are proposed: Supervised Learning (SL), Learning from Demonstration (LfD), and Reinforcement Learning (RL). There are also other methods that cannot be classified into these three areas and hence they have been placed in the other methods section. This research was conducted within three databases: Scopus, Web of Science, and Google Scholar. Accurate exclusion criteria were applied to screen the 2594 articles found (at the end 39 articles were selected). For each work, an evaluation of the model is made. CONCLUSION: Current research in cloth manipulation and dressing assistance focuses on learning-based robot control approach. Inferring the cloth state is integral to learning the manipulation and current research uses principles of Computer Vision to address the issue. This makes the larger problem of control robot based on learning data-intensive; therefore, a pressing need for standardized datasets representing different cloth shapes, types, materials, and human demonstrations (for LfD) exists. Simultaneously, efficient simulation capabilities, which closely model the deformation of clothes, are required to bridge the reality gap between the real-world and virtual environments for deploying the RL trial and error paradigm. Such powerful simulators are also vital to collect valuable data to train SL and LfD algorithms that will help reduce human workload.