Engineering and research communities are rapidly integrating AI into control system design, merging physics-based modeling, data-driven algorithms, and productivity tools to create faster, more ...

A paddle-wielding robot is so adept at playing table tennis that it is posing a tough challenge to elite human players and ...

Control systems shape the performance of technologies from autonomous vehicles to power plants. With MATLAB and Simulink, you can virtually model, design, and optimize controllers before deploying ...

Confidence is persuasive. In artificial intelligence systems, it is often misleading. Today's most capable reasoning models ...

Abstract: Integrating learning-based techniques, especially reinforcement learning, into robotics is promising for solving complex problems in unstructured environments. Most of the existing ...

Robust Reinforcement Learning-based model for UAV self-separation under Uncertainty. Hybrid; Amsterdam , Noord-Holland , Netherlands; Aerosp ...

Reinforcement learning has become the central approach for language models (LMs) to learn from environmental reward or feedback. In practice, the environmental feedback is usually sparse and delayed.

Abstract: Although recent research has made some progress in deep reinforcement learning based on raw pixels, the low sample efficiency remains a key challenge in this field. Existing solutions often ...

Over the past few years, AI systems have become much better at discerning images, generating language, and performing tasks within physical and virtual environments. Yet they still fail in ways that ...

Every year, NeurIPS produces hundreds of impressive papers, and a handful that subtly reset how practitioners think about scaling, evaluation and system design. In 2025, the most consequential works ...