Physical AI marks a transition from robots as programmed tools to robots as adaptable collaborators. That transition will ...

Database optimization has long relied on traditional methods that struggle with the complexities of modern data environments. These methods often fail to efficiently handle large-scale data, complex ...

Europe is standing at a pivotal crossroads in industrial history. According to NVIDIA CEO Jensen Huang, AI robotics ...

Trends such as industry-specific AI and a new data economy will affect physical AI in 2026, says a Universal Robots executive.

B, an open-source AI coding model trained in four days on Nvidia B200 GPUs, publishing its full reinforcement-learning stack ...

Welcome to the Stochastic Control for Continuous Time Portfolios project! This application uses Deep Reinforcement Learning to help you manage your investments smartly. You will learn how to adapt ...

(opens in a new window) (opens in a new window) (opens in a new window) (opens in a new window) Copy HelpAge International’s 2024–25 Learning Report explores how ageism interacts with other forms of ...

Abstract: A differential dynamic programming (DDP)-based framework for inverse reinforcement learning (IRL) is introduced to recover the parameters in the cost function, system dynamics, and ...

Download PDF Join the Discussion View in the ACM Digital Library Deep reinforcement learning (DRL) has elevated RL to complex environments by employing neural network representations of policies. 1 It ...

Abstract: Different kinds of compensation topologies in wireless power transfer (WPT) systems usually can only achieve constant voltage (CV) or constant current (CC) output at the same time when the ...