AI 中文总结
本文提出利用质量-多样性算法，在开放环境中高效发现稳健抓取配置，以生成多样化操作轨迹，提升机器人的泛化与适应能力。
AI 中文摘要
我们提出使用质量-多样性（QD）算法来解决开放环境中的机器人抓取操作任务。我们的方法能够高效发现广泛的稳健抓取配置，这些配置可作为在铰接物体上生成多样化抓取操作轨迹的可靠起点。由此产生的操作行为多样性增强了泛化性和适应性，使得在持续演变的开放世界环境中实现有效部署。
英文摘要
We propose to use Quality-Diversity (QD) algorithms to solve robotic prehensile manipulation tasks in open-ended environments. Our approach enables the efficient discovery of a wide range robust grasp configurations, which serve as reliable starting points for generating diverse prehensile manipulation trajectories on articulated objects. The resulting diversity in manipulation behaviors enhances generalization and adaptability, enabling effective deployment continuously evolving open-world settings.
Comments3 pages, 1 figure. Accepted at the 7th International Workshop on Intrinsically Motivated Open-ended Learning (IMOL 2025)
