Towards Human-Centered Construction Robotics: A Reinforcement Learning-Driven Companion Robot for Contextually Assisting Carpentry Workers
It addresses the need for collaborative robotics in the construction industry to support workers without replacing them, though it appears incremental as it builds on existing RL methods for human-robot interaction.
This paper tackles the problem of integrating robots into construction workflows by introducing a human-centered 'work companion rover' that assists carpentry workers, resulting in a prototype that enhances safety and workflow fluency through a contextual Reinforcement Learning framework.
In the dynamic construction industry, traditional robotic integration has primarily focused on automating specific tasks, often overlooking the complexity and variability of human aspects in construction workflows. This paper introduces a human-centered approach with a "work companion rover" designed to assist construction workers within their existing practices, aiming to enhance safety and workflow fluency while respecting construction labor's skilled nature. We conduct an in-depth study on deploying a robotic system in carpentry formwork, showcasing a prototype that emphasizes mobility, safety, and comfortable worker-robot collaboration in dynamic environments through a contextual Reinforcement Learning (RL)-driven modular framework. Our research advances robotic applications in construction, advocating for collaborative models where adaptive robots support rather than replace humans, underscoring the potential for an interactive and collaborative human-robot workforce.