Cloud-Based Hierarchical Imitation Learning for Scalable Transfer of Construction Skills from Human Workers to Assisting Robots
Publication: Journal of Computing in Civil Engineering
Volume 38, Issue 4
Abstract
Assigning repetitive and physically demanding construction tasks to robots can alleviate human workers’ exposure to occupational injuries, which often result in significant downtime or premature retirement. However, the successful delegation of construction tasks and the achievement of high-quality robot-constructed work requires transferring necessary dexterous and adaptive construction craft skills from workers to robots. Predefined motion planning scripts tend to generate rigid and collision-prone robotic behaviors in unstructured construction site environments. In contrast, imitation learning (IL) offers a more robust and flexible skill transfer scheme. However, the majority of IL algorithms rely on human workers repeatedly demonstrating task performance at full scale, which can be counterproductive and infeasible in the case of construction work. To address this concern, in this paper, we propose an immersive and Cloud Robotics-based virtual demonstration framework that serves two primary purposes. First, it digitalizes the demonstration process, eliminating the need for repetitive physical manipulation of heavy construction objects. Second, it employs a federated collection of reusable demonstrations that are transferable for similar tasks in the future and can, consequently, reduce the requirement for repetitive illustration of tasks by human agents. In addition, to enhance the trustworthiness, explainability, and ethical soundness of the robot training, this framework utilizes a hierarchical imitation learning (HIL) model to decompose human manipulation skills into sequential and reactive subskills. These two layers of skills are represented by deep generative models; these models enable adaptive control of robot action. The proposed framework has the potential to mitigate technical adoption barriers and facilitate the practical deployment of full-scale construction robots to perform a variety of tasks with human supervision. By delegating the physical strains of construction work to human-trained robots, this framework promotes the inclusion of workers with diverse physical capabilities and educational backgrounds within the construction industry.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request, including the ceiling installation demonstration trajectory data.
Acknowledgments
The authors would like to acknowledge the financial support for this research received from the US National Science Foundation (NSF) (Grant Nos. FW-HTF 2025805 and FW-HTF 2128623). Any opinions and findings in this paper are those of the authors and do not necessarily represent those of the NSF. The authors would also like to acknowledge the contribution of engineering technician Justin Roelofs for demonstrating the ceiling installation process and building the wooden models used in the physical robot experiments.
References
Abdo, N., H. Kretzschmar, and C. Stachniss. 2012. “From low-level trajectory demonstrations to symbolic actions for planning.” In Proc., ICAPS Workshop on Combining Task and Motion Planning for Real-World App, 29–36. State College, PA: Citeseer.
Adami, P., P. B. Rodrigues, P. J. Woods, B. Becerik-Gerber, L. Soibelman, Y. Copur-Gencturk, and G. Lucas. 2022. “Impact of VR-based training on human–robot interaction for remote operating construction robots.” J. Comput. Civ. Eng. 36 (3): 04022006. https://doi.org/10.1061/(ASCE)CP.1943-5487.0001016.
AGC (Associated General Contractors). 2016. Two-thirds of contractors have a hard time finding qualified craft workers to hire amid growing construction demand, national survey finds. Arlington, VA: AGC.
Aryan, A., F. Bosché, and P. Tang. 2021. “Planning for terrestrial laser scanning in construction: A review.” Autom. Constr. 125 (Mar): 103551. https://doi.org/10.1016/j.autcon.2021.103551.
Balaji, B., et al. 2016. “Brick: Towards a unified metadata schema for buildings.” In Proc., 3rd ACM Int. Conf. on Systems for Energy-Efficient Built Environments, 41–50. New York: Association for Computing Machinery.
Bandera, J. P., L. Molina-Tanco, J. A. Rodriguez, and A. Bandera. 2010. Architecture for a robot learning by imitation system. In Proc., Melecon 2010-2010 15th IEEE Mediterranean Electrotechnical Conf., 87–92. New York: IEEE.
Bauer, P., W. Lienhart, and S. Jost. 2021. “Accuracy investigation of the pose determination of a VR system.” Sensors 21 (5): 1622. https://doi.org/10.3390/s21051622.
Bello, S. A., L. O. Oyedele, O. O. Akinade, M. Bilal, J. M. D. Delgado, L. A. Akanbi, A. O. Ajayi, and H. A. Owolabi. 2021. “Cloud computing in construction industry: Use cases, benefits and challenges.” Autom. Constr. 122 (Feb): 103441. https://doi.org/10.1016/j.autcon.2020.103441.
BLS (US Bureau of Labor). 2023. “Industries at a glance, construction: NAICS 23.” Accessed June 16, 2023. https://www.bls.gov/iag/tgs/iag23.htm.
Brosque, C., and M. Fischer. 2022. “Safety, quality, schedule, and cost impacts of ten construction robots.” Constr. Rob. 6 (2): 163–186. https://doi.org/10.1007/s41693-022-00072-5.
Cai, J., A. Du, X. Liang, and S. Li. 2023. “Prediction-based path planning for safe and efficient human–robot collaboration in construction via deep reinforcement learning.” J. Comput. Civ. Eng. 37 (1): 04022046. https://doi.org/10.1061/(ASCE)CP.1943-5487.0001056.
Chen, Z. Y., Z. P. Fan, and M. Sun. 2015. “Behavior-aware user response modeling in social media: Learning from diverse heterogeneous data.” Eur. J. Oper. Res. 241 (2): 422–434. https://doi.org/10.1016/j.ejor.2014.09.008.
Cohen, Y., O. Bar-Shira, and S. Berman. 2021. “Motion adaptation based on learning the manifold of task and dynamic movement primitive parameters.” Robotica 39 (7): 1299–1315. https://doi.org/10.1017/S0263574720001186.
Coleman, D., I. Sucan, S. Chitta, and N. Correll. 2014. “Reducing the barrier to entry of complex robotic software: A Moveit! case study.” Preprint, submitted April 15, 2014. http://arxiv.org/abs/1404.3785.
DelPreto, J., J. I. Lipton, L. Sanneman, A. J. Fay, C. Fourie, C. Choi, and D. Rus. 2020. “Helping robots learn: A human-robot master-apprentice model using demonstrations via virtual reality teleoperation.” In Proc., 2020 IEEE Int. Conf. on Robotics and Automation (ICRA), 10226–10233. New York: IEEE.
de Luca, A. B., G. Zhang, X. Chen, and Y. Yu. 2022. “Mitigating data heterogeneity in federated learning with data augmentation.” Preprint, submitted June 20, 2022. http://arxiv.org/abs/2206.09979.
Delvinne, H. H., K. Hurtado, J. Smithwick, B. Lines, and K. Sullivan. 2020. “Construction workforce challenges and solutions: A national study of the roofing sector in the United States.” In Proc., Construction Research Congress 2020: Safety, Workforce, and Education, 529–537. Reston, VA: ASCE.
Deng, M., B. Fu, C. C. Menassa, and V. R. Kamat. 2023. “Learning-based personal models for joint optimization of thermal comfort and energy consumption in flexible workplaces.” Energy Build. 298 (Jun): 113438. https://doi.org/10.1016/j.enbuild.2023.113438.
Du, J., Y. Shi, Z. Zou, and D. Zhao. 2018. “CoVR: Cloud-based multiuser virtual reality headset system for project communication of remote users.” J. Constr. Eng. Manage. 144 (2): 04017109. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001426.
Fang, Y., and Y. K. Cho. 2016. “Real-time visualization of crane lifting operation in virtual reality.” In Vol. 11 of Proc., 16th Int. Conf. on Construction Applications of Virtual Reality, 13. Hong Kong: Hong Kong Univ. of Science and Technology.
Feng, C., V. R. Kamat, and C. C. Menassa. 2016. “Marker-assisted structure from motion for 3D environment modeling and object pose estimation.” In Proc., Construction Research Congress 2016, 2604–2613. Reston, VA: ASCE.
Feng, Y., J. Wang, H. Fan, and Y. Hu. 2021. “A BIM-based coordination support system for emergency response.” IEEE Access 9 (Jun): 68814–68825. https://doi.org/10.1109/ACCESS.2021.3077237.
Golparvar-Fard, M., A. Heydarian, and J. C. Niebles. 2013. “Vision-based action recognition of earthmoving equipment using spatio-temporal features and support vector machine classifiers.” Adv. Eng. Inf. 27 (4): 652–663. https://doi.org/10.1016/j.aei.2013.09.001.
Gunning, D. 2017. “Explainable artificial intelligence (xai), 2017.” In Defense advanced research projects agency (DARPA) project. Ft. Belvoir, VA: Defense Technical Information Center.
Haddadin, S., A. De Luca, and A. Albu-Schäffer. 2017. “Robot collisions: A survey on detection, isolation, and identification.” IEEE Trans. Rob. 33 (6): 1292–1312. https://doi.org/10.1109/TRO.2017.2723903.
Hamon, R., H. Junklewitz, and I. Sanchez. 2020. Robustness and explainability of artificial intelligence—From technical to policy solutions, EUR 30040. Luxembourg: Publications Office of the European Union.
Hayashi, K., S. Sakaino, and T. Tsuji. 2022. “An independently learnable hierarchical model for bilateral control-based imitation learning applications.” IEEE Access 10 (Apr): 32766–32781. https://doi.org/10.1109/ACCESS.2022.3155255.
He, J. 2017. “Learning from data heterogeneity: Algorithms and applications.” In Proc., IJCAI, 5126–5130. San Francisco: International Joint Conferences on Artificial Intelligence.
Hsiao, K., and T. Lozano-Perez. 2006 “Imitation learning of whole-body grasps.” In Proc., 2006 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 5657–5662. New York: IEEE.
Huang, L., Z. Zhu, and Z. Zou. 2023. “To imitate or not to imitate: Boosting reinforcement learning-based construction robotic control for long-horizon tasks using virtual demonstrations.” Autom. Constr. 146 (Feb): 104691. https://doi.org/10.1016/j.autcon.2022.104691.
Jeong, I., Y. Jang, J. Park, and Y. K. Cho. 2021. “Motion planning of mobile robots for autonomous navigation on uneven ground surfaces.” J. Comput. Civ. Eng. 35 (3): 04021001. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000963.
Jiao, Y., Y. Wang, S. Zhang, Y. Li, B. Yang, and L. Yuan. 2013. “A cloud approach to unified lifecycle data management in architecture, engineering, construction and facilities management: Integrating BIMs and SNS.” Adv. Eng. Inf. 27 (2): 173–188. https://doi.org/10.1016/j.aei.2012.11.006.
Karp, P. D., S. M. Paley, and I. Greenberg. 1994. “A storage system for scalable knowledge representation.” In Proc., Third Int. Conf. on Information and Knowledge Management, 97–104. New York: Association for Computing Machinery.
Kase, K., C. Paxton, H. Mazhar, T. Ogata, and D. Fox. 2020. “Transferable task execution from pixels through deep planning domain learning.” In Proc., 2020 IEEE Int. Conf. on Robotics and Automation (ICRA), 10459–10465. New York: IEEE.
Kim, P., and Y. K. Cho. 2017. “An automatic robust point cloud registration on construction sites.” In Computing in civil engineering 2017, 411–419. Reston, VA: ASCE.
Kingma, D. P., and J. Ba. 2014. “Adam: A method for stochastic optimization.” Preprint, submitted December 22, 2014. https://arxiv.org/abs/1412.6980.
Kingma, D. P., and M. Welling. 2013. “Auto-encoding variational bayes.” In Proc., 2nd Int. Conf. on Learning Representations (ICLR). Amherst, MA: Univ. of Massachusetts Amherst.
Kohler, M. D., R. W. Clayton, Y. Bozorgnia, E. Taciroglu, and R. Guy. 2022. “The community seismic network: Applications and expansion to 1200 stations.” In Vol. 2022 of Proc., AGU Fall Meeting Abstracts. Washington, DC: American Geophysical Union.
Kuhar, S., S. Cheng, S. Chopra, M. Bronars, and D. Xu. 2023. “Learning to discern: Imitating heterogeneous human demonstrations with preference and representation learning.” In Proc., Conf. on Robot Learning, 1437–1449. Cambridge, MA: Proceedings of Machine Learning Research.
Kyrarini, M., M. A. Haseeb, D. Ristić-Durrant, and A. Gräser. 2019. “Robot learning of industrial assembly task via human demonstrations.” Auton. Robots 43 (1): 239–257. https://doi.org/10.1007/s10514-018-9725-6.
Lee, D., S. Lee, N. Masoud, M. S. Krishnan, and V. C. Li. 2022. “Digital twin-driven deep reinforcement learning for adaptive task allocation in robotic construction.” Adv. Eng. Inf. 53 (Aug): 101710. https://doi.org/10.1016/j.aei.2022.101710.
Liang, C. J., and M. H. Cheng. 2023. “Trends in robotics research in occupational safety and health: A scientometric analysis and review.” Int. J. Environ. Res. Public Health 20 (10): 5904. https://doi.org/10.3390/ijerph20105904.
Liang, C. J., V. R. Kamat, and C. C. Menassa. 2020. “Teaching robots to perform quasi-repetitive construction tasks through human demonstration.” Autom. Constr. 120 (Dec): 103370. https://doi.org/10.1016/j.autcon.2020.103370.
Liang, C. J., T. H. Le, Y. Ham, B. R. Mantha, M. H. Cheng, and J. J. Lin. 2023. “Ethics of artificial intelligence and robotics in the architecture, engineering, and construction industry.” Preprint, submitted October 9, 2023. https://arxiv.org/abs/2310.05414.
Liang, C. J., X. Wang, V. R. Kamat, and C. C. Menassa. 2021. “Human–robot collaboration in construction: Classification and research trends.” J. Constr. Eng. Manage. 147 (10): 03121006. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002154.
Lin, X., Z. Huang, Y. Li, J. B. Tenenbaum, D. Held, and C. Gan. 2022. “Diffskill: Skill abstraction from differentiable physics for deformable object manipulations with tools.” Preprint, submitted March 21, 2022. http://arxiv.org/abs/2203.17275.
Liu, C., S. Shirowzhan, S. M. E. Sepasgozar, and A. Kaboli. 2019. “Evaluation of classical operators and fuzzy logic algorithms for edge detection of panels at exterior cladding of buildings.” Buildings 9 (2): 40. https://doi.org/10.3390/buildings9020040.
Liu, H., S. Nasiriany, L. Zhang, Z. Bao, and Y. Zhu. 2022. “Robot learning on the job: Human-in-the-loop autonomy and learning during deployment.” Preprint, submitted July 4, 2023. http://arxiv.org/abs/2211.08416.
Lundeen, K., V. Kamat, C. Menassa, and W. McGee. 2018. “Adaptive perception and modeling for robotized construction joint filling.” In Proc., Int. Symp. on Automation and Robotics in Construction (ISARC), 244–251. Oulu, Finland: International Association for Automation and Robotics in Construction.
Lundeen, K. M., V. R. Kamat, C. C. Menassa, and W. McGee. 2017. “Scene understanding for adaptive manipulation in robotized construction work.” Autom. Constr. 82 (Oct): 16–30. https://doi.org/10.1016/j.autcon.2017.06.022.
Luo, J., C. Xu, X. Geng, G. Feng, K. Fang, L. Tan, S. Schall, and S. Levine. 2023. “Multi-stage cable routing through hierarchical imitation learning.” IEEE Trans. Rob. 40 (Jan): 1476–1491. https://doi.org/10.1109/TRO.2024.3353075.
Makondo, N., B. Rosman, and O. Hasegawa. 2015. “Knowledge transfer for learning robot models via local Procrustes analysis.” In Proc., 2015 IEEE-RAS 15th Int. Conf. on Humanoid Robots (Humanoids), 1075–1082. New York: IEEE.
Mandal, S. K., G. Bhat, C. A. Patil, J. R. Doppa, P. P. Pande, and U. Y. Ogras. 2019. “Dynamic resource management of heterogeneous mobile platforms via imitation learning.” IEEE Trans. Very Large Scale Integr. VLSI Syst. 27 (12): 2842–2854. https://doi.org/10.1109/TVLSI.2019.2926106.
Mendieta, M., T. Yang, P. Wang, M. Lee, Z. Ding, and C. Chen. 2022. “Local learning matters: Rethinking data heterogeneity in federated learning.” In Proc., IEEE/CVF Conf. on Computer Vision and Pattern Recognition, 8397–8406. New York: IEEE.
Nakanishi, J., S. Itadera, T. Aoyama, and Y. Hasegawa. 2020. “Towards the development of an intuitive teleoperation system for human support robot using a VR device.” Adv. Rob. 34 (19): 1239–1253. https://doi.org/10.1080/01691864.2020.1813623.
Nasiriany, S., T. Gao, A. Mandlekar, and Y. Zhu. 2022. “Learning and retrieval from prior data for skill-based imitation learning.” Preprint, submitted November 15, 2022. http://arxiv.org/abs/2210.11435.
Neto, P., and N. Mendes. 2013. “Direct off-line robot programming via a common CAD package.” Rob. Auton. Syst. 61 (8): 896–910. https://doi.org/10.1016/j.robot.2013.02.005.
Paleja, R., and M. Gombolay. 2019. “Heterogeneous learning from demonstration.” In Proc., 14th ACM/IEEE Int. Conf. on Human-Robot Interaction (HRI), 730–732. New York: IEEE.
Pan, M., and W. Pan. 2020. “Stakeholder perceptions of the future application of construction robots for buildings in a dialectical system framework.” J. Manage. Eng. 36 (6): 04020080. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000846.
Park, S., H. Yu, C. C. Menassa, and V. R. Kamat. 2023. “A comprehensive evaluation of factors influencing acceptance of robotic assistants in field construction work.” J. Manage. Eng. 39 (3): 04023010. https://doi.org/10.1061/JMENEA.MEENG-5227.
Paszke, A., et al. 2019. “Pytorch: An imperative style, high-performance deep learning library.” In Advances in neural information processing systems, 32. Cambridge, MA: MIT Press.
Rawai, N. M., M. S. Fathi, M. Abedi, and S. Rambat. 2013. “Cloud computing for green construction management.” In Proc., 2013 Third Int. Conf. on Intelligent System Design and Engineering Applications, 432–435. New York: IEEE.
Ross, S., G. Gordon, and D. Bagnell. 2011. “A reduction of imitation learning and structured prediction to no-regret online learning.” In Proc., Fourteenth Int. Conf. on Artificial Intelligence and Statistics, 627–635. Maastricht, Netherlands: ML Research Press.
RSMeans. 2016. Building construction cost data. Norwell, MA: Construc-tion Publishers & Consultants.
Ryu, J., M. M. Diraneyya, C. T. Haas, and E. Abdel-Rahman. 2021. “Analysis of the limits of automated rule-based ergonomic assessment in bricklaying.” J. Constr. Eng. Manage. 147 (2): 04020163. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001978.
Schaal, S., J. Peters, J. Nakanishi, and A. Ijspeert. 2005. “Learning movement primitives.” In Proc., Robotics Research. The Eleventh Int. Symp.: With 303 Figures, 561–572. Berlin: Springer.
Shayesteh, S., and H. Jebelli. 2021. “Investigating the impact of construction robots autonomy level on workers’ cognitive load.” In Proc., Canadian Society of Civil Engineering Annual Conf., 255–267. Singapore: Springer.
Shi, H., R. Li, X. Bai, Y. Zhang, L. Min, D. Wang, X. Lu, Y. Yan, and Y. Lei. 2023. “A review for control theory and condition monitoring on construction robots.” J. Field Rob. 40 (4): 934–954. https://doi.org/10.1002/rob.22156.
Sing, M. C., V. W. Tam, I. W. Fung, and H. J. Liu. 2017. “Critical analysis of construction workforce sustainability in a developed economy–case study in Hong Kong.” Proc. Inst. Civ. Eng. Eng. Sustainability 171 (7): 342–350. https://doi.org/10.1680/jensu.17.00007.
Sokas, R. K., X. S. Dong, and C. T. Cain. 2019. “Building a sustainable construction workforce.” Int. J. Environ. Res. Public Health 16 (21): 4202. https://doi.org/10.3390/ijerph16214202.
Sun, Y., I. Jeelani, and M. Gheisari. 2023. “Safe human-robot collaboration in construction: A conceptual perspective.” J. Saf. Res. 86 (Sep): 39–51. https://doi.org/10.1016/j.jsr.2023.06.006.
Tolani, D., A. Goswami, and N. I. Badler. 2000. “Real-time inverse kinematics techniques for anthropomorphic limbs.” Graphical Models 62 (5): 353–388. https://doi.org/10.1006/gmod.2000.0528.
Wan, W., Y. He, J. Liu, H. Lu, and H. Zhang. 2020. “Application of ‘BIM+’ architecture based on cloud technology in intelligent management of rail transit.” In Resilience and sustainable transportation systems, 474–484. Reston, VA: ASCE.
Wang, X., C. J. Liang, C. C. Menassa, and V. R. Kamat. 2021a. “Interactive and immersive process-level digital twin for collaborative human–robot construction work.” J. Comput. Civ. Eng. 35 (6): 04021023. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000988.
Wang, X., S. Wang, C. C. Menassa, V. R. Kamat, and W. McGee. 2023a. “Automatic high-level motion sequencing methods for enabling multi-tasking construction robots.” Autom. Constr. 155 (Aug): 105071. https://doi.org/10.1016/j.autcon.2023.105071.
Wang, X., H. Yu, W. McGee, C. C. Menassa, and V. R. Kamat. 2023b. “Enabling BIM-driven robotic construction workflows with closed-loop digital twins.” Preprint, submitted June 16, 2023. http://arxiv.org/abs/2306.09639.
Wang, Y., C. C. Beltran-Hernandez, W. Wan, and K. Harada. 2021b. “Robotic imitation of human assembly skills using hybrid trajectory and force learning.” In Proc., 2021 IEEE Int. Conf. on Robotics and Automation (ICRA), 11278–11284. New York: IEEE.
Wang, Z., Y. Gan, and X. Dai. 2020. “An environment state perception method based on knowledge representation in dual-arm robot assembly tasks.” Int. J. Intell. Rob. Appl. 4 (2): 177–190. https://doi.org/10.1007/s41315-020-00128-1.
Wilder, R. 2013. “Big increase in Gulf coast projects equals big demand for skilled workers.” The Conrnerstone. Northbrook, IL: Scott Foresman.
Wos, P., and R. Dindorf. 2023. “Develop and Implement a masonry algorithm control in a bricklaying robot.” In Vol. 2949 of Proc., AIP Conf. Woodbury, NY: American Institute of Physics.
Wu, H., H. Li, X. Fang, and X. Luo. 2022. “A survey on teaching workplace skills to construction robots.” Expert Syst. Appl. 205 (Nov): 117658. https://doi.org/10.1016/j.eswa.2022.117658.
Xie, F., A. Chowdhury, M. De Paolis Kaluza, L. Zhao, L. Wong, and R. Yu. 2020. “Deep imitation learning for bimanual robotic manipulation.” In Vol. 33 of Proc., Advances in Neural Information Processing Systems, 2327–2337. Cambridge, MA: MIT Press.
Xu, G., M. Li, C. H. Chen, and Y. Wei. 2018. “Cloud asset-enabled integrated IoT platform for lean prefabricated construction.” Autom. Constr. 93 (Sep): 123–134. https://doi.org/10.1016/j.autcon.2018.05.012.
Xu, L., C. Feng, V. R. Kamat, and C. C. Menassa. 2020. “A scene-adaptive descriptor for visual SLAM-based locating applications in built environments.” Autom. Constr. 112 (Apr): 103067. https://doi.org/10.1016/j.autcon.2019.103067.
Yamada, T., J. Tatsuno, and H. Kobayashi. 2001. “A practical way to apply the natural human like communication to human-robot interface.” In Proc., 10th IEEE Int. Workshop on Robot and Human Interactive Communication. ROMAN 2001 (Cat. No. 01TH8591), 158–163). New York: IEEE.
Yang, S., P. Liu, and N. E. Pears. 2023. “Benchmarking of robot arm motion planning in cluttered environments.” In Proc., 28th Int. Conf. on Automation and Computing (ICAC2023). New York: IEEE.
Yang, S., X. Yu, and Y. Zhou. 2020. “LSTM and GRU neural network performance comparison study: Taking yelp review dataset as an example.” In Proc., 2020 Int. Workshop on Electronic Communication and Artificial Intelligence (IWECAI), 98–101. New York: IEEE.
Yu, H., V. R. Kamat, C. C. Menassa, W. McGee, Y. Guo, and H. Lee. 2023a. “Grip state recognition for enabling safe human-robot object handover in physically collaborative construction work.” In Computing in civil engineering 2023, 787–795. Reston, VA: ASCE.
Yu, H., V. R. Kamat, C. C. Menassa, W. McGee, Y. Guo, and H. Lee. 2023b. “Mutual physical state-aware object handover in full-contact collaborative human-robot construction work.” Autom. Constr. 150 (Apr): 104829. https://doi.org/10.1016/j.autcon.2023.104829.
Zha, Y., S. Bhambri, and L. Guan. 2021. “Contrastively learning visual attention as affordance cues from demonstrations for robotic grasping.” In Proc., 2021 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 7835–7842. New York: IEEE.
Zhang, D., Q. Li, Y. Zheng, L. Wei, D. Zhang, and Z. Zhang. 2021. “Explainable hierarchical imitation learning for robotic drink pouring.” IEEE Trans. Autom. Sci. Eng. 19 (4): 3871–3887. https://doi.org/10.1109/TASE.2021.3138280.
Zhu, Q., T. Zhou, P. Xia, and J. Du. 2022. “Robot planning for active collision avoidance in modular construction: Pipe skids example.” J. Constr. Eng. Manage. 148 (10): 04022114. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002374.
Information & Authors
Information
Published In
Journal of Computing in Civil Engineering
Volume 38 • Issue 4 • July 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Sep 20, 2023
Accepted: Jan 3, 2024
Published online: Apr 17, 2024
Published in print: Jul 1, 2024
Discussion open until: Sep 17, 2024
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.