Bastian Wibranek

Bio: Bastian Wibranek is an academic researcher from University of Texas at San Antonio. The author has contributed to research in topics: Modular design & Proof of concept. The author has co-authored 1 publications.

A review for control theory and condition monitoring on construction robots

Abstract: The application of robotic technologies in building construction leads to great convenience and productivity improvement, and construction robots (CRs) bring enormous opportunities for the way we conduct design and construction. To get a better understanding of the trends and track the application of CRs for on‐site conditions, this paper conducts a systematic review of control models and status monitoring of CRs, which are two key aspects that determine construction accuracy and efficiency. Control accuracy and flexibility are primary needs for CRs applied in different scenes, so the control methods based on driving models are vitally important. Status monitoring on CRs contains knowledge in fault detection, intelligence maintenance, and fault‐tolerant control, and multiple objectives need to be met and optimized in the whole drive chain. Moreover, the state‐of‐the‐art is comprehensively summarized, and new insights are also provided to carry on promising researches.

A survey on teaching workplace skills to construction robots

Abstract: The construction industry is seeking a robotic revolution to meet increasing demands for productivity, quality, and safety. Typically, construction robots are usually pre-programmed for a single task, such as painting. Their behavior is fixed when they leave the factory. However, it is difficult to pre-program all capabilities (referred to as workplace skills) that construction workers may require. Construction robots are expected to have the same ability of skill learning as human apprentices, allowing them to acquire a wide range of workplace skills from experienced workers and eventually complete relevant construction tasks autonomously. However, workplace skill learning of robots has rarely been investigated in the construction industry. This survey reviews state-of-the-art approaches to help robots learn skills from human demonstrations. To begin, the workplace skill is represented as ‘Know That’ and ‘Know How’ problems. ‘Know That’ is a high-level task planning ability aimed at understanding human activities from demonstrations. ‘Know How’ refers to the ability to learn specific actions for completing the construction task. Sematic methods and learn from demonstration (LfD) methods are reviewed to tackle these two problems. Finally, we discuss the open issues of past research, present future directions, and highlight the survey’s knowledge contributions. We believe that this survey will provide a new perspective on robots in the construction industry and inspire more discussions about skill learning of construction robots.

A review on reinforcement learning for contact-rich robotic manipulation tasks

Abstract: Research and application of reinforcement learning in robotics for contact-rich manipulation tasks have exploded in recent years. Its ability to cope with unstructured environments and accomplish hard-to-engineer behaviors has led reinforcement learning agents to be increasingly applied in real-life scenarios. However, there is still a long way ahead for reinforcement learning to become a core element in industrial applications. This paper examines the landscape of reinforcement learning and reviews advances in its application in contact-rich tasks from 2017 to the present. The analysis investigates the main research for the most commonly selected tasks for testing reinforcement learning algorithms in both rigid and deformable object manipulation. Additionally, the trends around reinforcement learning associated with serial manipulators are explored as well as the various technological challenges that this machine learning control technique currently presents. Lastly, based on the state-of-the-art and the commonalities among the studies, a framework relating the main concepts of reinforcement learning in contact-rich manipulation tasks is proposed. The final goal of this review is to support the robotics community in future development of systems commanded by reinforcement learning, discuss the main challenges of this technology and suggest future research directions in the domain.