Towards Human-Centered Construction Robotics: A Reinforcement Learning-Driven Companion Robot for Contextually Assisting Carpentry Workers
On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 37 references that are in the map. First posted on arXiv 27 Mar 2024.
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10 of 37 references.
References · 10
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- 2025Force-Based Robotic Imitation Learning: A Two-Phase Approach for Construction Assembly Tasks
- 2026A Taxonomy of Construction Task Activities for Robot Workers ↗
- 2024Contextual Affordances for Safe Exploration in Robotic Scenarios
- 2026Harnessing human expertise for high-precision robotic assembly in industrialized construction: A sample-efficient installer-in-the-loop interactive reinforcement learning framework
- 2025Sample-Efficient Robot Skill Learning for Construction Tasks: Benchmarking Hierarchical Reinforcement Learning and Vision-Language-Action VLA Model
- 2026Zero-shot adaptable task planning for autonomous construction robots: a comparative study of lightweight single and multi-AI agent systems