The DOI matches its Crossref record; 174 of the 174 references it cites were found in a registry or an index.
Proactive human–robot collaboration: Mutual-cognitive, predictable, and self-organising perspectives
Open accessAbstract
Human–Robot Collaboration (HRC) has a pivotal role in smart manufacturing for strict requirements of human-centricity, sustainability, and resilience. However, existing HRC development mainly undertakes either a human-dominant or robot-dominant manner, where human and robotic agents reactively perform operations by following pre-defined instructions, thus far from an efficient integration of robotic automation and human cognition. The stiff human–robot relations fail to be qualified for complex manufacturing tasks and cannot ease the physical and psychological load of human operators. In response to these realistic needs, this paper presents our arguments on the obvious trend, concept, systematic architecture, and enabling technologies of Proactive HRC, serving as a prospective vision and research topic for future work in the human-centric smart manufacturing era. Human–robot symbiotic relation is evolving with a 5C intelligence — from Connection, Coordination, Cyber, Cognition to Coevolution, and finally embracing mutual-cognitive, predictable, and self-organising intelligent capabilities, i.e., the Proactive HRC. With proactive robot control, multiple human and robotic agents collaboratively operate manufacturing tasks, considering each others’ operation needs, desired resources, and qualified complementary capabilities. This paper also highlights current challenges and future research directions, which deserve more research efforts for real-world applications of Proactive HRC. It is hoped that this work can attract more open discussions and provide useful insights to both academic and industrial practitioners in their exploration of human–robot flexible production.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems
Lee, J., Bagheri, B., & Kao, H.-A. (2015) · Manufacturing Letters · doi:10.1016/j.mfglet.2014.12.001 · Cited by 5,113
Proceedings. 1985 IEEE International Conference on Robotics and Automation
(1985) · doi:10.1109/robot.1985 · Cited by 3,673
Industry 4.0 and Industry 5.0—Inception, conception and perception
Xu, X., Lu, Y., Vogel-Heuser, B., & Wang, L. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.10.006 · Cited by 2,557
Self-citation2008 IEEE/RSJ International Conference on Intelligent Robots and Systems
(2008) · doi:10.1109/iros13647.2008 · Cited by 2,183
Industry 5.0: Prospect and retrospect
Leng, J., Sha, W., Wang, B., Zheng, P., Zhuang, C., Liu, Q., Wuest, T., Mourtzis, D., & Wang, L. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.09.017 · Cited by 1,300
Self-citation2014 IEEE/RSJ International Conference on Intelligent Robots and Systems
(2014) · doi:10.1109/iros20755.2014 · Cited by 1,293
2011 IEEE International Conference on Robotics and Automation
(2011) · doi:10.1109/icra15140.2011 · Cited by 1,209
46th IEEE conference on decision and control
(2007) · 2007 46th IEEE Conference on Decision and Control · doi:10.1109/cdc.2007.4434001 · Cited by 835
2007 IEEE International Conference on Image Processing
(2007) · doi:10.1109/icip10338.2007 · Cited by 791
Cited by
Towards new-generation human-centric smart manufacturing in Industry 5.0: A systematic review
Zhang, C., Wang, Z., Zhou, G., Chang, F., Ma, D., Jing, Y., Cheng, W., Ding, K., & Zhao, D. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102121 · Cited by 283
Self-citationLou, S., Zhang, Y., Tan, R., & Lv, C. (2024) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2023.102706 · Cited by 84
Human-in-the-Loop Robot Learning for Smart Manufacturing: A Human-Centric Perspective
Chen, H., Li, S., Fan, J., Duan, A., Yang, C., Navarro-Alarcon, D., & Zheng, P. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3528051 · Cited by 67
Self-citationLiang, X., Sheng, L., & Cai, J. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103591 · Cited by 8
Metadata
- Authors
- Li, S., Zheng, P., Liu, S., Wang, Z., Wang, X. V., Zheng, L., & Wang, L.
- Venue
- Robotics and Computer-Integrated Manufacturing
- Publisher
- Elsevier BV
- Published
- 2023-06
- Volume
- 81
- Pages
- 102510
- ISSN
- 0736-5845 (ISSN-L)
- OpenAlex
- W4313453017
- Cited by
- 265
- References
- 174
- FWCI
- 21.92
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Proactive human–robot collaboration: Mutual-cognitive, predictable, and self-organising perspectives. https://app.cochat.ai/papers/10.1016/j.rcim.2022.102510
- The paper
- Li, S., Zheng, P., Liu, S., Wang, Z., Wang, X. V., Zheng, L., & Wang, L. (2023). Proactive human–robot collaboration: Mutual-cognitive, predictable, and self-organising perspectives. Robotics and Computer-Integrated Manufacturing. https://doi.org/10.1016/j.rcim.2022.102510
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.