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
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…
Hierarchical Reinforcement Learning (HRL) is a potent paradigm for addressing long-horizon sequential decision-making in swarm confrontation. However, its strategic capabilities are often bottlenecked by high-level policies that struggle to reason over the dynamic, variable-sized observations of oth…
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
In mobile robotics, effectively covering known terrains is essential. While grid-based methods surpass exact cell decomposition in path length and multi-robot scalability, they face challenges in irregular areas. Here we develop a model for shortening coverage paths in arbitrary environments using g…