Highlights from science policy Interface sessions at the one health congress 2020. [PDF]
One Health Outlook, 2021 Vanlangendonck C +2 more
europepmc +1 more source
Multi‐Material Additive Manufacturing of Soft Robotic Systems: A Comprehensive Review
Advanced Robotics Research, EarlyView.This review explores the transformative role of multi‐material additive manufacturing (MMAM) in the development of soft robotic systems. It presents current techniques, materials, and design strategies that enable functionally graded and adaptive structures.
Ritik Raj +2 more
wiley +1 more source
Correction: Public engagement with science-Origins, motives and impact in academic literature and science policy. [PDF]
PLoS OneWeingart P, Joubert M, Connoway K.
europepmc +1 more source
The Future of Research in Cognitive Robotics: Foundation Models or Developmental Cognitive Models?
Advanced Robotics Research, EarlyView.Research in cognitive robotics founded on principles of developmental psychology and enactive cognitive science would yield what we seek in autonomous robots: the ability to perceive its environment, learn from experience, anticipate the outcome of events, act to pursue goals, and adapt to changing circumstances without resorting to training with ...
David Vernon
wiley +1 more source
Artificial intelligence integration in the drug lifecycle and in regulatory science: policy implications, challenges and opportunities. [PDF]
Front PharmacolOualikene-Gonin W +7 more
europepmc +1 more source
Hard‐Magnetic Soft Millirobots in Underactuated Systems
Advanced Robotics Research, EarlyView.This review provides a comprehensive overview of hard‐magnetic soft millirobots in underactuated systems. It examines key advances in structural design, physics‐informed modeling, and control strategies, while highlighting the interplay among these domains.
Qiong Wang +4 more
wiley +1 more source
Model uncertainty, the COVID-19 pandemic, and the science-policy interface. [PDF]
R Soc Open SciThorén H, Gerlee P.
europepmc +1 more source
Grounding Large Language Models for Robot Task Planning Using Closed‐Loop State Feedback
Advanced Robotics Research, EarlyView.BrainBody‐Large Language Model (LLM) introduces a hierarchical, feedback‐driven planning framework where two LLMs coordinate high‐level reasoning and low‐level control for robotic tasks. By grounding decisions in real‐time state feedback, it reduces hallucinations and improves task reliability.
Vineet Bhat +4 more
wiley +1 more source