Science

Researchers have developed a new hierarchical decision-making framework that allows humanoid robots to learn complex and stable boxing techniques through competitive self-training.

Researchers have developed a new system that automatically generates publication-quality diagrams and plots, streamlining the visual communication of complex scientific findings.

New research suggests that integrating assistive robots into universally designed workplaces can actively reduce the stigma often faced by people with disabilities in professional settings.

A new analysis reveals that traditional graph theory methods can be surprisingly competitive with complex machine learning models for predicting missing links in real-world economic and financial networks.

A new approach combines sensor data and machine learning to accurately recognize human activities while safeguarding patient privacy in resource-constrained healthcare settings.

Researchers have developed an artificial intelligence system that leverages the power of language to autonomously design molecules and peptides, pushing the boundaries of biological engineering.

A new framework enables multi-agent systems to learn reward functions that prioritize resilience and sustained performance even when faced with disruptions.

As artificial intelligence systems proliferate, understanding the true uniqueness of individual models becomes critical for efficient deployment and reliable performance.

A new framework enhances human-robot teamwork by predicting user intent and dynamically adjusting assistance levels in real-time.

As AI systems become increasingly pervasive, a robust mechanism for independent assurance is critical to ensure their safety, fairness, and alignment with societal values.