To realize rapid, safe and optimal avoidance for spacecraft in complex threat situations and
incomplete-information conditions, and considering the problems in existing hierarchical …

In many existing multi-agent reinforcement learning tasks, each agent observes all the other
agents from its own perspective. In addition, the training process is centralized, namely the …

Sustainable cities are envisioned to have economic and industrial steps toward reducing
pollution. Many real-world applications such as autonomous vehicles, transportation, traffic …

Reinforcement Learning-(RL-) based motion planning has recently shown the potential to
outperform traditional approaches from autonomous navigation to robot manipulation. In this …

The navigation system is one of the most prominent and essential part of robotic autonomy.
Along with more advanced navigation techniques are developed for autonomous robotics, it …

We present an end-to-end differentiable learning algorithm for multi-agent navigation
policies. Compared with prior model-free learning algorithms, our method leads to a …

Shepherding is the problem of guiding a group of passive sheep agents (a flock) from some
start position to a goal region by influencing the sheep motion with active guiding agents …

Multi-agent path finding in dynamic crowded environments is of great academic and
practical value for multi-robot systems in the real world. To improve the effectiveness and …

Multi-agent defensive convoy helps provide critical safety for a leader agent. Escort agents
work by coordinating their actions to protect the leader agent in the convoy. This paper …

Safely navigating environments with obstacles that move stochastically cannot be
guaranteed by automation, due to the motion planning becoming computationally …