The field of human-robot collaboration is moving towards more personalized and adaptive interactions, with a focus on enhancing trust and well-being in industrial settings. Recent research has explored the use of physiological signals, federated learning, and generative AI to improve trust evaluation and mental state assessment. The integration of these technologies has the potential to create more effective and safe human-robot collaboration systems. Noteworthy papers include: The Align the GAP paper, which proposes a unified framework for multi-task remote physiological measurement and test-time personalized adaptation. The PPTP paper, which introduces a novel framework for trust prediction in human-robot collaboration using synchronized multimodal physiological signals. The Chain-of-Trust paper, which proposes a progressive trust evaluation framework enabled by generative AI.