@article{22999,
  abstract     = {The safety of Reinforcement Learning (RL)-based controllers has become a prominent research area in recent years, with various approaches being proposed to address this critical issue. Runtime Safety Assurance (RSA) methods for RL, such as Shielded RL, provide formal safety guarantees by preventing agents from taking unsafe actions and suggesting safe alternatives when necessary. However, previous surveys and reviews on RSA for RL have not thoroughly analysed the challenges and applications within the industrial sector. This study builds on existing state-of-the-art research on Shielded RL methods, emphasising its contributions to industrial applications and offering a domain-specific categorisation. This categorisation highlights the primary industrial domains utilising Shielded RL, detailing the optimised functions achieved by RL and the safety functions ensured by the shield. Additionally, the study presents a categorisation based on environmental features, enabling readers to assess the complexity of the problems addressed by the techniques studied. The shield’s attributes are analysed for each work, identifying key trends in their application, including their adaptability to new scenarios. Finally, a basic categorisation model for Shielded RL approaches, grounded in industrial safety standards, is introduced. This model serves as a baseline for future studies aiming to evaluate the maturity level of the works reviewed.},
  author       = {Odriozola-Olalde, Haritz and Cano Cordoba, Filip and Könighofer, Bettina and Arana-Arexolaleiba, Nestor and Zamalloa, Maider and Perez-Cerrolaza, Jon},
  issn         = {2667-3053},
  journal      = {Intelligent Systems with Applications},
  keywords     = {Reinforcement learning, Runtime safety assurance, Shield, Safety, Industrial application},
  publisher    = {Elsevier},
  title        = {{Shielded reinforcement learning for industrial applications: A systematic literature survey}},
  doi          = {10.1016/j.iswa.2026.200729},
  volume       = {32},
  year         = {2026},
}

@inproceedings{12976,
  abstract     = {3D printing based on continuous deposition of materials, such as filament-based 3D printing, has seen widespread adoption thanks to its versatility in working with a wide range of materials. An important shortcoming of this type of technology is its limited multi-material capabilities. While there are simple hardware designs that enable multi-material printing in principle, the required software is heavily underdeveloped. A typical hardware design fuses together individual materials fed into a single chamber from multiple inlets before they are deposited. This design, however, introduces a time delay between the intended material mixture and its actual deposition. In this work, inspired by diverse path planning research in robotics, we show that this mechanical challenge can be addressed via improved printer control. We propose to formulate the search for optimal multi-material printing policies in a reinforcement
learning setup. We put forward a simple numerical deposition model that takes into account the non-linear material mixing and delayed material deposition. To validate our system we focus on color fabrication, a problem known for its strict requirements for varying material mixtures at a high spatial frequency. We demonstrate that our learned control policy outperforms state-of-the-art hand-crafted algorithms.},
  author       = {Liao, Kang and Tricard, Thibault and Piovarci, Michael and Seidel, Hans-Peter and Babaei, Vahid},
  booktitle    = {2023 IEEE International Conference on Robotics and Automation},
  issn         = {1050-4729},
  keywords     = {reinforcement learning, deposition, control, color, multi-filament},
  location     = {London, United Kingdom},
  pages        = {12345--12352},
  publisher    = {IEEE},
  title        = {{Learning deposition policies for fused multi-material 3D printing}},
  doi          = {10.1109/ICRA48891.2023.10160465},
  volume       = {2023},
  year         = {2023},
}