AI Advances in Robotics, Healthcare, and Supply Chains

Recent advancements in artificial intelligence (AI) are pushing the boundaries of what is possible in various industries, from robotics and healthcare to supply chains. Researchers have made significant breakthroughs in reinforcement learning, multimodal data integration, and uncertainty-aware prediction, which could have far-reaching consequences for businesses and society.

One of the key challenges in robotics is enabling robots to learn in real-world environments. A new study published on arXiv investigates what specific design choices enable successful online reinforcement learning on physical robots. The researchers conducted 100 real-world training runs on three distinct robotic platforms and found that some widely used defaults can be harmful, while a set of robust, readily adopted design choices within standard RL practice yield stable learning across tasks and hardware. These results provide the first large-sample empirical study of such design choices, enabling practitioners to deploy online RL with lower engineering effort.

In the healthcare sector, the integration of multimodal data, such as images and text, is crucial for accurate diagnoses and effective treatments. However, existing methods struggle to handle heterogeneous modalities, limiting their applicability. To address this, researchers have developed the Multi-Modal Prior-data Fitted Network (MMPFN), which extends the TabPFN to handle tabular and non-tabular modalities in a unified manner. MMPFN comprises per-modality encoders, modality projectors, and pre-trained foundation models, and has demonstrated state-of-the-art performance on medical and general-purpose multimodal datasets.

Another area where AI is making a significant impact is in supply chain management. Accurate delivery delay prediction is critical for maintaining operational efficiency and customer satisfaction. Researchers have introduced a multi-task deep learning model for delivery delay duration prediction in the presence of significant imbalanced data. The model embeds high-dimensional shipment features with dedicated embedding layers for tabular data and uses a classification-then-regression strategy to predict the delivery delay duration for on-time and delayed shipments.

In addition to these specific applications, researchers are also exploring the broader implications of AI on society. A review of controlled trials and independent validations across software engineering, clinical documentation, and clinical decision support has quantified the expectation-realisation gap for agentic AI systems. The study found that there are systematic discrepancies between pre-deployment expectations and post-deployment outcomes, driven by workflow integration friction, verification challenges, and unmet assumptions about user behavior.

Finally, a new method for exploring anti-aging literature via convex topics and large language models has been proposed. The method produces stable, fine-grained topics by selecting exemplars from the data and guaranteeing a global optimum. Applied to about 12,000 PubMed articles on aging and longevity, the method uncovers topics validated by medical experts, spanning from molecular mechanisms to dietary supplements, physical activity, and gut microbiota.

These advances in AI research have the potential to transform various industries and improve outcomes in healthcare, supply chain management, and beyond. As the field continues to evolve, it is essential to consider the broader implications of AI on society and to address the challenges and limitations associated with its development and deployment.

Sources:

• "What Matters for Simulation to Online Reinforcement Learning on Real Robots" (arXiv:2602.20220v1)
• "MultiModalPFN: Extending Prior-Data Fitted Networks for Multimodal Tabular Learning" (arXiv:2602.20223v1)
• "Exploring Anti-Aging Literature via ConvexTopics and Large Language Models" (arXiv:2602.20224v1)
• "Uncertainty-Aware Delivery Delay Duration Prediction via Multi-Task Deep Learning" (arXiv:2602.20271v1)
• "Quantifying the Expectation-Realisation Gap for Agentic AI Systems" (arXiv:2602.20292v1)