Towards Robust Prehensile Manipulation in Open-Ended Environments

Abstract

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.

arXiv comment3 pages, 1 figure. Accepted at the 7th International Workshop on Intrinsically Motivated Open-ended Learning (IMOL 2025)