activity
20242026
most citedDeep Dive into Model-free Reinforcement Learning for Biological and Robotic Systems: Theory and Practice

3 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2026

Interdisciplinary Workshop on Mechanical Intelligence: Summary Report

Victoria A. Webster-Wood, Nicholas Gravish, Amir Alavi +37

This report provides a summary of the outcomes of the Interdisciplinary Workshop on Mechanical Intelligence held in 2024. Mechanical Intelligence (MI) represents the phenomenon tha…

physics.bio-ph2026

Steer'n Roll: A Stereoscopic Flow-Sensing Strategy for Planktonic Prey Detection and Capture

Tommaso Redaelli, Eva Kanso, Christophe Eloy

Planktonic organisms such as copepods sense swimming prey and sinking food particles through the hydrodynamic disturbances they generate. However, because these flow fields are oft…

physics.bio-ph2025

Self-reorganization and Information Transfer in Massive Schools of Fish

Haotian Hang, Chenchen Huang, Alex Barnett +1

The remarkable cohesion and coordination observed in moving animal groups and their collective responsiveness to threats are thought to be mediated by scale-free correlations, wher…

physics.flu-dyn2024

Hierarchical equivariant graph neural networks for forecasting collective motion in vortex clusters and microswimmers

Alec J. Linot, Haotian Hang, Eva Kanso +1

Data-driven modeling of collective dynamics is a challenging problem because emergent phenomena in multi-agent systems are often shaped by long-range interactions among individuals…

cs.RO2024★ 3 cited

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…