Teams of embodied AI-enabled agents are critical for applications in extreme and highly
dynamic environments. Develo** robust controllers for such agents requires a deep …

Efficient multi-agent pathfinding (MAPF) is becoming increasingly relevant in real-world
scenarios. MAPF aims to minimize the travel time for robots operating in a shared …

Multi-Agent Path Finding (MAPF) in crowded environments presents a challenging problem
in motion planning, aiming to find collision-free paths for all agents in the system. MAPF …

Multi-agent path finding (MAPF) holds significant practical relevance in numerous real-world
applications involving fleets of mobile robots. The efficiency of such systems is directly …

Multi-Agent Path Finding (MAPF) in crowded environments presents a challenging problem
in motion planning, aiming to find collision-free paths for all agents in the system. MAPF …

Insect colonies, birds, and fish successfully coordinate themselves to make collective
decisions through purely local interactions. Their behaviors have inspired the development …

In many real-world applications of robot fleets, the robots must be able to operate efficiently
in a dynamic environment where obstacles stochastically appear. While the related Multi …

The Multi-Agent Path Finding (MAPF) problem focuses on deriving collision-free paths for
multiple agents in a shared environment. However, the traditional formulation of the MAPF …

With the continuous promotion of China's energy revolution and the goal of “carbon peaking
and carbon neutral” by 2020, it has become an unstoppable trend for new energy sources to …

This work presents a pipeline to infer semantic goals of multiple humans based on their
motion cues as perceived by multiple robots. Experiments were conducted to gather data in …