Industrial Robots: Why They can both Replace and Augment Human Labor

Since their emergence, industrial robots have advanced from mechanical‑replication devices toward human‑robot collaborative intelligent systems, driven by iterative progress in sensing and artificial‑intelligence technologies. This paper classifies their evolution into three phases: teaching‑reproduction, perception‑driven operation, and intelligent collaboration. It examines core technologies including robot sensing, machine vision, and force control, and clarifies how AI and human‑robot collaboration underpin flexible manufacturing. Based on industrial realities, three development trends are outlined: deep large‑model integration for autonomous decision‑making, expanded application scenarios enabled by flexible bionic mechanisms, and full‑production‑line collaboration via the Industrial Internet. Key industry challenges involve deficient core‑component technologies, high deployment costs for SMEs, interdisciplinary talent shortages, incomplete industrial standards, and ethical issues stemming from labor transformation. This study argues that smart‑manufacturing transformation emphasizes human‑machine complementary coordination rather than simple machine substitution of labor. These findings offer theoretical references for industrial‑robot R&D and standardized industry promotion.

Authors

Publication Details

Journal
Applied and Computational Engineering
Published
2026-11-10
DOI
https://doi.org/10.54254/2755-2721/2026.ld38818
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

Industrial Robots: Why They can both Replace and Augment Human Labor

Suiyi Zhang
Applied and Computational Engineering
Robot Manipulation and Learning
article

Industrial Robots: Why They can both Replace and Augment Human Labor

Suiyi Zhang
article en

Abstract

Since their emergence, industrial robots have advanced from mechanical‑replication devices toward human‑robot collaborative intelligent systems, driven by iterative progress in sensing and artificial‑intelligence technologies. This paper classifies their evolution into three phases: teaching‑reproduction, perception‑driven operation, and intelligent collaboration. It examines core technologies including robot sensing, machine vision, and force control, and clarifies how AI and human‑robot collaboration underpin flexible manufacturing. Based on industrial realities, three development trends are outlined: deep large‑model integration for autonomous decision‑making, expanded application scenarios enabled by flexible bionic mechanisms, and full‑production‑line collaboration via the Industrial Internet. Key industry challenges involve deficient core‑component technologies, high deployment costs for SMEs, interdisciplinary talent shortages, incomplete industrial standards, and ethical issues stemming from labor transformation. This study argues that smart‑manufacturing transformation emphasizes human‑machine complementary coordination rather than simple machine substitution of labor. These findings offer theoretical references for industrial‑robot R&D and standardized industry promotion.

Applied and Computational EngineeringVol. 279(1)
Robot Manipulation and Learning
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