Results 171 to 180 of about 1,052,376 (334)
, 2023 Reinforcement learning, especially deep reinforcement learning, has made many advances in the last decade. Similarly, great strides have been made in multi-agent reinforcement learning. Systems of cooperative autonomous robots are increasingly being used,
de Gooijer, Jessica (author)
core
3D Printing of Stretchable, Compressible and Conductive Porous Polyurethane for Soft Robotics
Advanced Materials Technologies, EarlyView.A 3D‐printable porous dopamine‐polyurethane acrylate elastomer results in conductive, stretchable, and compressible structures that can be metallized in situ through catechol‐mediated silver reduction. The resulting material function as both compliant soft robot with a and strain sensors without complex assemblies, enabling fully 3D‐printed soft ...
Ouriel Bliah +3 more
wiley +1 more source
At Home Detection of Ovarian Health Biomarker in Menstruation Blood
Advanced Materials Technologies, EarlyView.A lateral flow assay enables the detection of anti‐Müllerian hormone directly in unprocessed menstrual blood using silica‐gold nanoshells and smartphone‐assisted machine learning analysis. The platform supports decentralized, user‐operated testing in wearable and dipstick formats, highlighting the potential of menstrual blood as a non‐invasive matrix ...
Lucas Dosnon +3 more
wiley +1 more source
This paper presents a novel reinforcement learning (RL)-based planning scheme for optimized robotic management of biotic stresses in precision agriculture.
Khosravi, Mahsa +8 more
core
Data‐Efficient Electromagnetic Surrogate Solver Through Dissipative Relaxation Transfer Learning
Advanced Optical Materials, EarlyView.Dissipative relaxation transfer learning (DIRTL) enables data‐efficient training of electromagnetic surrogate solvers by pretraining data generated with artificial material loss before fine‐tuning on target lossless data. The framework suppresses resonant outlier effects during early training, allowing effective adaptation to high‐amplitude resonances ...
Sunghyun Nam +2 more
wiley +1 more source
Learning Highly Dynamic Skills Transition for Quadruped Jumping Through Constrained Space
Advanced Robotics Research, EarlyView.A quadruped robot masters dynamic jumps through constrained spaces with animal‐inspired moves and intelligent vision control. This hierarchical learning approach combines imitation of biological agility with real‐time trajectory planning. Although legged animals are capable of performing explosive motions while traversing confined spaces, replicating ...
Zeren Luo +6 more
wiley +1 more source
Object-sensitive Deep Reinforcement Learning
Deep reinforcement learning has become popular over recent years, showing superiority on different visual-input tasks such as playing Atari games and robot navigation.
Katia Sycara, Rahul Iyer, Yuezhang Li
core +1 more source
Stable Imitation of Multigait and Bipedal Motions for Quadrupedal Robots Over Uneven Terrains
Advanced Robotics Research, EarlyView.How are quadrupedal robots empowered to execute complex navigation tasks, including multigait and bipedal motions? Challenges in stability and real‐world adaptation persist, especially with uneven terrains and disturbances. This article presents an imitation learning framework that enhances adaptability and robustness by incorporating long short‐term ...
Erdong Xiao +3 more
wiley +1 more source
Emulating perceptual development in deep reinforcement learning
The process of learning in infants differs from the traditional reinforcement learning (RL) methods in several aspects. The biggest difference is that RL assumes a stationary world and an agent with fixed sensory and motor abilities.
Asada, M. +3 more
core +1 more source
An Open-Loop Baseline for Reinforcement Learning Locomotion Tasks [PDF]
In search of a simple baseline for Deep Reinforcement Learning in locomotion tasks, we propose a model-free open-loop strategy. By leveraging prior knowledge and the elegance of simple oscillators to generate periodic joint motions, it achieves ...
Raffin, Antonin +5 more
core +1 more source