5 accepted papers
In swarm confrontation, robots must swiftly formulate strategies in transient environments, a challenge well-suited for multi-agent reinforcement learning (MARL). However, existing platforms suffer from the lack of comprehensive confrontation scenario modeling and scalable frameworks, hindering MARL
In this article, we address the shape formation problem for massive robot swarms in environments where external localization systems are unavailable.Achieving this task effectively with solely onboard measurements is still scarcely explored and faces some practical challenges.To solve this challengi…
Multi-agent reinforcement learning (MARL) provides a flexible solution for tackling task and motion planning challenges, particularly in swarm confrontation scenarios. By customizing termination conditions for diverse tasks, event-driven MARL reduces decision jitter caused by frequent task switching…
In AI-assisted decision-making, human decision-makers finalize decisions by taking into account both their human confidence and AI confidence regarding specific outcomes. In practice, they often exhibit heterogeneous cognitive capacities, causing their confidence to deviate, sometimes significantly…
In swarm robotics, confrontation scenarios, including strategic confrontations, require efficient decision– making that integrates discrete commands and continuous actions. Traditional task and motion planning methods separate decision–making into two layers, but their unidirectional structure fails