Results 221 to 230 of about 197,676 (281)

Multi modal hierarchical reinforcement learning framework for dynamic sports sponsorship optimization. [PDF]

Sci Rep
Yu Q.
europepmc   +1 more source

Reinforcement Learning and Deep Neural Networks for PI Controller Tuning

, 2019
William John Shipman, Loutjie Coetzee
openalex   +1 more source

Soft Robotic Snake with Tunable Undulatory Gait for Efficient Underwater Locomotion

Advanced Robotics Research, EarlyView.
This study designs an underwater soft snake robot using 3D‐printed soft actuators, controlled by specific signals to generate sinusoidal undulation. Results show a positive correlation between speed and swing amplitude, with optimal performance at 2/3π phase offset, PLA tail, 1.2 voltage growth rate, and 6s undulation period achieving a maximum speed ...
Huichen Ma, Junjie Zhou, Raye Yeow
wiley   +1 more source

The necessity of multimodal feedback for learning effective pedagogical policies with reinforcement learning. [PDF]

Sci Rep
Che L, Guo P, Isleem HF, Wang Z.
europepmc   +1 more source

Design of Automatic Error Correction System for English Translation based on Reinforcement Learning Algorithm

Hui Liu
openalex   +2 more sources

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, Quan‐Chen Song, Jia‐Yang Juang   +2 more
wiley   +1 more source

Reinforcement learning of a biflagellate model microswimmer. [PDF]

Eur Phys J E Soft Matter
Bulusu S, Zöttl A.
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

Model-free optical processors using in situ reinforcement learning with proximal policy optimization. [PDF]

Light Sci Appl
Li Y, Chen S, Gong T, Ozcan A.
europepmc   +1 more source

Reinforcement Learning-Based Finite-Time Sliding-Mode Control in a Human-in-the-Loop Framework for Pediatric Gait Exoskeleton

Meixiang Sang, Jyotindra Narayan
openalex   +1 more source