Deep reinforcement learning has produced many success stories in recent years. Some
example fields in which these successes have taken place include mathematics, games …

In this paper, we present a solution to a design problem of control strategies for multi-agent
cooperative transport. Although existing learning-based methods assume that the number of …

We consider task allocation for multi-object transport using a multi-robot system, in which
each robot selects one object among multiple objects with different and unknown weights …

One hot-researched application of multi-robot systems is cooperative object transport, a task
in which multiple robots collaboratively move an object that is too larger, bulky or heavy for a …

We consider the problem of collectively transporting multiple objects using air–ground
multirobot teams. The objective is to find the optimal matching between the objects and …

Multi-object transport using multi-robot systems has the potential for diverse practical
applications such as delivery services owing to its efficient individual and scalable …

Multi-robot cooperative transport is crucial in logistics, housekeeping, and disaster
response. However, it poses significant challenges in environments where objects of various …

Artificial Neural Networks (ANN) used to control autonomous robots must be trained to
guarantee their proper functioning. In most cases, unsupervised computational algorithms …

Multi-robot transportation has attracted attention in robotics and can be applied in fields such
as delivery, logistics, and search and rescue. While previous studies have successfully …

抄録 In order to design a controller for cooperative transportation by swarm robots, there is
an approach to optimize a Neural Network (NN) as a controller using reinforcement learning …