A new longitudinal study reveals how individuals with visual impairments progressively refine their collaborative strategies with a navigation robot, building trust and effectively sharing control.
As digital twins gain the ability to act on their modeled environments, understanding their capabilities-and governing their actions-becomes critically important.
This review explores how integrating recommender system techniques into social robots can create more engaging and effective user experiences.
Current autonomous AI systems often prove fragile in real-world scenarios, and this paper proposes a system-theoretic approach and a catalog of design patterns to address that instability.
New research details a machine learning approach to accurately estimate human trust levels during robotic collaboration, paving the way for more intuitive and effective teamwork.
As artificial intelligence rapidly evolves, psychology is emerging as a critical discipline for understanding its impact on humans and, crucially, for guiding its future development.
A new approach integrates semantic concepts into robotic imitation learning, dramatically improving sample efficiency and accelerating the learning process.
Researchers introduce a probabilistic framework for evaluating consciousness in artificial intelligence, offering initial assessments of current large language models.
New research explores the evolving dynamics of human-AI relationships, revealing the key factors that drive attachment to conversational agents.
Researchers have developed an AI-driven system that automates the creation of complex biological circuits, potentially accelerating advances in synthetic biology and biotechnology.