Machine learning for active matter
The availability of large datasets has boosted the application of machine learning in many
fields and is now starting to shape active-matter research as well. Machine learning …
fields and is now starting to shape active-matter research as well. Machine learning …
Swarm slam: Challenges and perspectives
A robot swarm is a decentralized system characterized by locality of sensing and
communication, self-organization, and redundancy. These characteristics allow robot …
communication, self-organization, and redundancy. These characteristics allow robot …
Swarm robotics: Past, present, and future [point of view]
Swarm robotics deals with the design, construction, and deployment of large groups of
robots that coordinate and cooperatively solve a problem or perform a task. It takes …
robots that coordinate and cooperatively solve a problem or perform a task. It takes …
Reinforcement learning with artificial microswimmers
Artificial microswimmers that can replicate the complex behavior of active matter are often
designed to mimic the self-propulsion of microscopic living organisms. However, compared …
designed to mimic the self-propulsion of microscopic living organisms. However, compared …
Bio-inspired collision avoidance in swarm systems via deep reinforcement learning
Autonomous vehicles have been highlighted as a major growth area for future transportation
systems and the deployment of large numbers of these vehicles is expected when safety …
systems and the deployment of large numbers of these vehicles is expected when safety …
Empirical assessment and comparison of neuro-evolutionary methods for the automatic off-line design of robot swarms
Neuro-evolution is an appealing approach to generating collective behaviors for robot
swarms. In its typical application, known as off-line automatic design, the neural networks …
swarms. In its typical application, known as off-line automatic design, the neural networks …
Federated reinforcement learning for collective navigation of robotic swarms
The recent advancement of deep reinforcement learning (DRL) contributed to robotics by
allowing automatic controller design. The automatic controller design is a crucial approach …
allowing automatic controller design. The automatic controller design is a crucial approach …
Disentangling automatic and semi-automatic approaches to the optimization-based design of control software for robot swarms
Optimization-based design is an effective and promising approach to realizing collective
behaviours for robot swarms. Unfortunately, the domain literature often remains vague about …
behaviours for robot swarms. Unfortunately, the domain literature often remains vague about …
Simulation-only experiments to mimic the effects of the reality gap in the automatic design of robot swarms
A Ligot, M Birattari - Swarm Intelligence, 2020 - Springer
The reality gap—the discrepancy between reality and simulation—is a critical issue in the off-
line automatic design of control software for robot swarms, as well as for single robots. It is …
line automatic design of control software for robot swarms, as well as for single robots. It is …
Automatic collective motion tuning using actor-critic deep reinforcement learning
Collective behaviours such as swarm formation of autonomous agents offer the advantages
of efficient movement, redundancy, and potential for human guidance of a single swarm …
of efficient movement, redundancy, and potential for human guidance of a single swarm …