Reinforcement learning upgrade helps robots stay steady in chaotic environments
An enhanced asynchronous actor-critic framework trained in simulation and on a physical mobile robot showed faster convergence, higher throughput, and ...
An enhanced asynchronous actor-critic framework trained in simulation and on a physical mobile robot showed faster convergence, higher throughput, and ...
A scoping review in Discover Artificial Intelligence finds that only 32 of 5,594 retrieved records explicitly anchor AI or robotics ...
A technical survey finds that reliable physical AI depends on co-designing learned world models, uncertainty representation, prediction horizons and closed-loop ...
A peer-reviewed review proposes IDEAI, a framework for representing, checking and revising the intent that guides embodied AI actions in ...
A reinforcement-learning method combining contraction theory was tested in simulation and on three robot platforms, providing stability guarantees and improving ...
A new meta-reinforcement learning framework enables quadruped robots to maintain stable locomotion even when multiple joints lock simultaneously, dramatically outperforming ...
A new analysis argues that solid-state batteries could transform robotics, revealing that a single commercial robot consumes an EV-equivalent 60 ...
Researchers combined artificial potential field trajectory planning with a discrete integral terminal sliding mode controller to let a Mecanum-wheeled omnidirectional ...
Researchers developed lightweight neural networks and a new RGB-D dataset that let robots detect accurate grasps for fruits of varying ...
A comprehensive new review traces how diffusion models, the generative engines behind modern image synthesis, have become the dominant force ...
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© 2026 Scienmag - Science Magazine