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A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models
L Da, J Turnau, TP Kutralingam, A Velasquez… - arxiv preprint arxiv …, 2025 - arxiv.org
Deep Reinforcement Learning (RL) has been explored and verified to be effective in solving
decision-making tasks in various domains, such as robotics, transportation, recommender …
decision-making tasks in various domains, such as robotics, transportation, recommender …
Neuro-Symbolic AI: Explainability, Challenges, and Future Trends
X Zhang, VS Sheng - arxiv preprint arxiv:2411.04383, 2024 - arxiv.org
Explainability is an essential reason limiting the application of neural networks in many vital
fields. Although neuro-symbolic AI hopes to enhance the overall explainability by leveraging …
fields. Although neuro-symbolic AI hopes to enhance the overall explainability by leveraging …
Bridging the Gap: Representation Spaces in Neuro-Symbolic AI
X Zhang, VS Sheng - arxiv preprint arxiv:2411.04393, 2024 - arxiv.org
Neuro-symbolic AI is an effective method for improving the overall performance of AI models
by combining the advantages of neural networks and symbolic learning. However, there are …
by combining the advantages of neural networks and symbolic learning. However, there are …
[PDF][PDF] NeuroStrata: Harnessing Neurosymbolic Paradigms for Improved Design, Testability, and Verifiability of Autonomous CPS
Autonomous cyber-physical systems (CPSs) leverage AI for perception, planning, and
control but face trust and safety certification challenges due to inherent uncertainties. The …
control but face trust and safety certification challenges due to inherent uncertainties. The …