Correction: Morphological symmetry-aware generalized policy network for deep reinforcement learning

Exploring deep reinforcement learning acceleration by superscaling data augmentation via branched fractal symmetries

Ryan Vander Stelt, Cleiver Ruiz-Martinez, Caeden Rosen, Blake Hull, Juan Rojas

Learning deep reinforcement learning (DRL) policies directly in physical robots remains bottlenecked by slow wall-clock training times. We present preliminary research on Branched Euclidean Group Fractal Symmetries , a trajectory-level augmentation framework that super-scales gro…Read more

Morphological symmetry-aware generalized policy network for deep reinforcement learning

Ryo Hakoda, Yubin Liu, Matthew Hwang, Yoshihiro Sato, Jun Takamatsu, Katsushi Ikeuchi, et al.

Exploiting the morphological symmetry of robotic systems, such as humanoid and quadruped robots, is a promising direction for improving robot learning. In deep reinforcement learning (DRL) for robot control, prior studies have leveraged such symmetry to improve learning efficienc…Read more

Structural predictors and latent maturity regimes of robotic readiness in global health systems: evidence from machine learning-based latent clustering and class prediction

Moumita Mukherjee, Raja Hashim Ali

Background The systematic integration of robotics into health service delivery systems requires periodic assessment of robotic readiness in terms of digital-health maturity regimes across countries. The current study aims to cluster 169 countries into maturity regimes and classif…Read more

Editorial: Advanced sensing, learning and control for effective human-robot interaction

Zhenyu Lu, Lu Chen, Yanpeng Guan, Jing Luo, Chao Zeng