In this paper, we propose a hierarchical multi-agent reinforcement learning framework for coordinating robot teams in warehouse environments under communication loss. We partition the robot team into groups, with centralized coordination within each group and distributed coordination across groups. Each group uses a re...
Wei-Hao Sun, Ge-Hui Xu, Andreas A. Malikopoulos· 0 citations
Learning-based driving planners are usually trained and evaluated in open loop against logged trajectories. In closed loop, a trajectory with small displacement error can still stall the vehicle, steer it into a conflict with surrounding agents, or be executed with abrupt braking. We introduce Closed-Loop Refinement an...
Huai-Jin Hu, Shan-Ting Wang, Zhong-Yu Mo et al.· 0 citations
A closed-loop framework for autonomous magnetic microrobot navigation that separates long-range geometric planning from short-range reactive control is presented, and a modular framework for autonomous magnetic microrobot navigation in complex biological environments is established.
Yan-Da Yang, Max Sokolich, F. Kırmızıtaş et al.· 3 citations
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