Towards Human-Centered Construction Robotics: A Reinforcement Learning-Driven Companion Robot for Contextually Assisting Carpentry Workers

TL;DR

This paper presents a human-centered construction robot that uses reinforcement learning to adaptively assist carpentry workers, enhancing safety and collaboration in dynamic construction environments.

Contribution

It introduces a novel RL-driven modular robot system designed for context-aware assistance, emphasizing human-robot collaboration in construction tasks.

Findings

Prototype demonstrates effective mobility and safety features.

Reinforcement learning enables adaptive, context-aware assistance.

Supports safer and more efficient construction workflows.

Abstract

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.