Science

This review details a software engineering framework for creating AI agents that effectively leverage codified knowledge from human experts to tackle complex tasks.

Researchers are drawing inspiration from the biological nervous system to create robotic control architectures that are more adaptable, efficient, and robust in dynamic environments.

New research reveals how an operator’s experience level fundamentally changes their approach to remotely supervising and intervening with robotic systems.

A new survey examines how large language models are evolving from passive text generators into proactive agents capable of planning, acting, and learning in dynamic environments.

New research explores how carefully designed prompts can significantly improve a robot’s ability to navigate social spaces safely and effectively.

A new framework integrates computational experiments and causal reasoning to unpack the complexities of modern systems, offering insights into phenomena like rider behavior on food delivery platforms.

A new augmented reality system visually communicates a robot’s capabilities and limitations, fostering more effective and intuitive collaboration.

Balancing speed and accuracy in digital investigations requires a new approach that leverages the strengths of both artificial intelligence and human expertise.

A new analysis challenges the notion of ‘interpretable’ AI as uncovering inherent model logic, instead framing it as a dynamic process shaped by interactions between humans and machines.

A new framework leverages probabilistic modeling to enable collaborative robots to better understand and respond to human intent in industrial settings.