Titulo Estágio
Learning from Human Demonstration for Tool Positioning in Stone-Cutting Machinery
Áreas de especialidade
Sistemas Inteligentes
Local do Estágio
CISUC
Enquadramento
The stone processing industry — including the cutting and shaping of materials such as marble, granite, and engineered stone — is undergoing a transformative shift driven by Industry 4.0 technologies. While modern cutting machines now incorporate advanced sensor systems, CNC controllers, and automation interfaces, a large portion of critical decision-making during operation still depends on skilled human operators.
These operators handle complex and delicate tasks, such as:
• Positioning the cutting tool based on stone geometry, texture, and defects,
• Adjusting cutting parameters in real time depending on vibration, noise, tool wear, or material hardness, and
• Making safety-critical judgments under changing environmental and operational conditions.
Traditional automation using hard-coded logic (PLC) or fixed control algorithms (e.g., PID) cannot cope effectively with the variability in stone types, slab defects, custom orders, or tool dynamics. This often leads to inefficiencies, downtime, and inconsistent quality, particularly in short production batches or customized cuts.
Despite advancements in automation and sensor technologies, the positioning of cutting tools in the stone-processing industry still relies heavily on the tacit knowledge of experienced human operators. Tool alignment requires nuanced understanding of stone geometry, surface defects, and setup precision—skills difficult to encode using rule-based systems or conventional control logic. Existing automation approaches fail to generalize across variable slab types and setup conditions, leading to inefficiencies and inconsistent quality. A lack of interpretable AI systems capable of learning from expert behavior further hinders the adoption of intelligent solutions in this high-precision domain.
Objetivo
To design, develop, and evaluate a human-in-the-loop AI system that learns from expert demonstrations to perform precise and adaptive cutting tool positioning in stone-cutting machinery.
The system will be trained using Learning from Demonstration techniques and will operate in live or simulated environments using sensor inputs from the machinery.
Research Objectives:
1. Analyze current operator practices in tool positioning, including error sources and setup variability.
2. Identify and integrate relevant sensor data (e.g., geometry scans, images, alignment markers) into a positioning framework.
3. Develop a human-in-the-loop learning system using techniques such as imitation learning to replicate expert positioning.
4. Implement and validate the AI system in simulated or real-world setups with actual machinery or testbeds.
5. Evaluate system performance in terms of accuracy, adaptability, safety, and operator trust.
Plano de Trabalhos - Semestre 1
1- State of the art [Sept – Oct]
2- Problem statement, research aims and objectives [Nov]
3- Design and first implementation of the AI system [Nov – Jan]
4- Thesis proposal writing [Dec – Jan]
Plano de Trabalhos - Semestre 2
5- Improvement of the AI system [Feb – Apr]
6- Experimental Tests [Apr – May]
7- Paper writing [May – Jun]
8- Thesis writing [Jan – Jul]
Condições
The work should take place at the Centre for Informatics and Systems of the University of Coimbra (CISUC) at the Department of Informatics Engineering of the University of Coimbra.
Observações
References
[1] 1. Argall, B. D., Chernova, S., Veloso, M., & Browning, B. (2009). A survey of robot learning from demonstration. Robotics and Autonomous Systems, 57(5), 469–483. https://doi.org/10.1016/j.robot.2008.10.024
[2] Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., & Amodei, D. (2017). Deep reinforcement learning from human preferences. Advances in Neural Information Processing Systems (NeurIPS), 30. https://arxiv.org/abs/1706.03741
[3] Reddy, S., Dragan, A., & Levine, S. (2019). Sqil: Imitation learning via regularized behavioral cloning. arXiv preprint arXiv:1905.11108. https://arxiv.org/abs/1905.11108
[4] Amershi, S., Cakmak, M., Knox, W. B., & Kulesza, T. (2014). Power to the people: The role of humans in interactive machine learning. AI Magazine, 35(4), 105–120. https://doi.org/10.1609/aimag.v35i4.2513
Orientador
Luís Miguel Machado Lopes Macedo
macedo@dei.uc.pt 📩