50 citations · 139 across the 5 of their papers we have counts for
6 papers
Navigation of a Three-Link Microswimmer via Deep Reinforcement Learning
Yuyang Lai, Sina Heydari, On Shun Pak +1
Motile microorganisms develop effective swimming gaits to adapt to complex biological environments. Translating this adaptability to smart microrobots presents significant challeng…
Deep Dive into Model-free Reinforcement Learning for Biological and Robotic Systems: Theory and Practice
Yusheng Jiao, Feng Ling, Sina Heydari +3
Animals and robots exist in a physical world and must coordinate their bodies to achieve behavioral objectives. With recent developments in deep reinforcement learning, it is now p…
Active tail flexion in concert with passive hydrodynamic forces improves swimming speed and efficiency
Haotian Hang, Sina Heydari, John H. Costello +1
Fish typically swim by periodic bending of their bodies. Bending seems to follow a universal rule; it occurs at about one-third from the posterior end of the fish body with a maxim…
Learning to swim in potential flow
Yusheng Jiao, Feng Ling, Sina Heydari +3
Fish swim by undulating their bodies. These propulsive motions require coordinated shape changes of a body that interacts with its fluid environment, but the specific shape coordin…
School cohesion, speed, and efficiency are modulated by the swimmers flapping motion
Sina Heydari, Eva Kanso
Fish schools are ubiquitous in marine life. Although flow interactions are thought to be beneficial for schooling, their exact effects on the speed, energetics, and stability of th…
Sea star inspired crawling and bouncing
Sina Heydari, Amy Johnson, Olaf Ellers +2
The oral surface of sea stars is lined with arrays of tube feet that enable them to achieve highly controlled locomotion on various terrains. The activity of the tube feet is orche…