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

Yuning Wu, Jiaying Wei, Jean Oh, Daniel Cardoso Llach

TL;DR

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.

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.

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

TL;DR

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.

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.
Paper Structure (18 sections, 2 equations, 9 figures, 2 tables)

This paper contains 18 sections, 2 equations, 9 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background and Related Works
  3. Assistive robots in various domains
  4. Robot navigation and mobility on construction site
  5. Reinforcement learning for social robot navigation
  6. Problem Statement
  7. Robotic support scenarios and functions
  8. MDP formulation
  9. Approaches
  10. Hardware system design
  11. Mapping and localization on unstructured site
  12. Worker detection and tracking
  13. Hierarchical motion planning
  14. Contextual fine-tuning of generic RL model
  15. Experiments and Results
  16. ...and 3 more sections

Figures (9)

  • Figure 1: Site observation captures of the carpentry formwork activitity and construction site condition
  • Figure 2: The robotic support scenarios and functions
  • Figure 3: The modular framework design
  • Figure 4: Hardware design of the robot
  • Figure 5: Grid map from gmapping (left) and ours (right)
  • ...and 4 more figures