Compared with single robots, Multi-Robot Systems (MRS) can perform missions more efficiently due to the presence of multiple members with diverse capabilities. However, deploying an MRS in wide real-world environments is still challenging due to uncertain and various obstacles (e.g., building clusters and trees). With a limited understanding of environmental uncertainty on performance, an MRS cannot flexibly adjust its behaviors (e.g., teaming, load sharing, trajectory planning) to ensure both environment adaptation and task accomplishments. In this work, a novel joint preference landscape learning and behavior adjusting framework (PLBA) is designed. PLBA efficiently integrates real-time human guidance to MRS coordination and utilizes Sparse Variational Gaussian Processes with Varying Output Noise to quickly assess human preferences by leveraging spatial correlations between environment characteristics. An optimization-based behavior-adjusting method then safely adapts MRS behaviors to environments. To validate PLBA's effectiveness in MRS behavior adaption, a flood disaster search and rescue task was designed. 20 human users provided 1764 feedback based on human preferences obtained from MRS behaviors related to "task quality", "task progress", "robot safety". The prediction accuracy and adaptation speed results show the effectiveness of PLBA in preference learning and MRS behavior adaption.

翻译：与单机器人相比，多机器人系统因其成员具备多样化能力，能够更高效地执行任务。然而，在广阔的现实环境中部署多机器人系统仍面临挑战，主要源于不确定且多样化的障碍物（如建筑群和树木）。由于对环境不确定性如何影响系统性能的认知有限，多机器人系统无法灵活调整其行为（如团队协作、负载分配、轨迹规划），从而难以同时确保环境适应性与任务完成度。本研究设计了一种新颖的联合偏好景观学习与行为调整框架。该框架通过稀疏变分高斯过程与可变输出噪声模型，有效整合实时人类指导至多机器人协同过程，并利用环境特征间的空间相关性快速评估人类偏好。随后，基于优化的行为调整方法能够安全地使多机器人系统行为适应环境变化。为验证该框架在多机器人行为适应方面的有效性，本研究设计了洪水灾害搜救任务。20位人类用户基于多机器人行为在"任务质量"、"任务进度"与"机器人安全性"三个维度提供了1764条偏好反馈。预测精度与适应速度的实验结果表明，该框架在偏好学习与多机器人行为适应方面具有显著效能。