Abstract Machine learning (ML) has revolutionized robotics by enhancing perception,
adaptability, decision-making and more, enabling robots to work in complex scenarios …

The study of microswimmers' behavior, including their self-propulsion, interactions with the
environment, and collective phenomena, has received significant attention over the past few …

Active flow control (AFC) through deep reinforcement learning (DRL) is computationally
demanding. To address this, a masked deep neural network (MDNN), aiming to replace the …

Swimming at the microscale has recently garnered substantial attention due to the
fundamental biological significance of swimming microorganisms and the wide range of …

Machine learning algorithms offer a tool to boost mobility and flexibility of a synthetic
microswimmer, hence may help us design truly smart microrobots. In this work, we design a …

We use the Deep Q-Network with reinforcement learning to investigate the emergence of
odd elasticity in an elastic microswimmer model. For an elastic microswimmer, it is …

Substantial efforts have exploited reinforcement learning (RL) in the development of micro-
robotic locomotion. These RL-powered micro-robots are capable of learning a locomotory …

The autonomous swimming of fish in a complex flow environment is a nonlinear and intricate
system, which is the focus and challenge in various fields. This study proposed a novel …

Aquatic organisms are known for their ability to generate efficient propulsion with low energy
expenditure. While existing research has sought to leverage bio-inspired structures to …

Machine learning and artificial intelligence have recently represented a popular paradigm
for designing and optimizing robotic systems across various scales. Recent studies have …