240 ACKNOWLEDGEMENTS The authors thank NONHUMAN for providing laboratory facilities and infras- tructure support that enabled this research. We are grateful to the open-source robotics community, particularly the developers of the SO-ARM100 platform and the open-source robot learning library [9], whose accessible tools and collaborative spirit made these experiments possible. We also acknowledge PUCP for institutional support. REFERENCES BBVA Research. (2023). Peru: Mining sector outlook 2022. February 3, 2023. (PDF report). Health and Safety Authority (HSA). (n.d.). Clearing Blocked Crushers. Quarrying: Crushing, Sizing & Screening guidance. Health and Safety Executive (HSE). (n.d.). Safe operation and use of mobile jaw crushers. Quarries guidance (UK). Mine Safety and Health Administration (MSHA). (2023). Fatality Report: September 8, 2023 Fatality - Final Report. U.S. Department of Labor. Mine Safety and Health Administration (MSHA). (n.d.). Confined Space - Safety Alert. U.S. Department of Labor. Metso. (2011). Crushing and Screening Handbook (5th ed.). (AusIMM-hosted hand- book PDF). Zhao, T. Z., Kumar, V., Levine, S., & Finn, C. (2023). Learning Fine-Grained Biman- ual Manipulation with Low-Cost Hardware. Proceedings of Robotics: Science and Systems (RSS) XIX, Daegu, Republic of Korea. DOI: 10.15607/RSS.2023.XIX.016 Chen, L., Jin, S., Wang, H., & Zhang, L. (2024). ExACT: An End-to-End Autonomous Excavator System Using Action Chunking With Transformers. ICRA 2024 Future of Construction Workshop. arXiv:2405.05861 LeRobot Team. (2026). LeRobot: An Open-Source Library for End-to-End Robot Learning. ICLR 2026 Poster. Available at: https://github.com/huggingface/ lerobot Knight, R., Kooijmans, P., Cadene, R., Alibert, S., Aractingi, M., Aubakirova, D., Zouitine, A., Martino, R., Palma, S., Pascal, C., & Wolf, T. (2024). Standard Open SO- 100 & SO-101 Arms. GitHub. https://github.com/TheRobotStudio/SO-ARM100 Physical Intelligence (Black, K., Brown, N., Driess, D., et al.). (2024). π0: A Vision- Language-Action Flow Model for General Robot Control. arXiv:2410.24164 Physical Intelligence (Black, K., Brown, N., Darpinian, J., et al.). (2025). π0.5: A Vision-Language-Action Model with Open-World Generalization. arXiv:2504.16054 Shukor, M., Aubakirova, D., Capuano, F., Kooijmans, P., Palma, S., Zouitine, A., Aractingi, M., Pascal, C., Russi, M., Marafioti, A., Alibert, S., Cord, M., Wolf, T., & Cadène, R. (2025). SmolVLA: A VisionLanguage-Action Model for Affordable and Efficient Robotics. arXiv:2506.01844 Chi, C., Xu, Z., Feng, S., Cousineau, E., Du, Y., Burchfiel, B., Tedrake, R., & Song, S. (2024). Diffusion Policy: Visuomotor Policy Learning via Action Diffusion. The International Journal of Robotics Research. DOI: 10.1177/02783649241273668. arXiv:2303.04137 Kim, H., & Choi, Y. (2022). Lab Scale Model Experiment of Smart Hopper System to Remove Blockages Using Machine Vision and Collaborative Robot. Applied Sciences, 12(2), 579.
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