Towards Robust Prehensile Manipulation in Open-Ended Environments

2026-10-05

AI Summary

This study addresses the limited robustness and generalization of robotic grasping in open environments by innovatively introducing Quality-Diversity (QD) algorithms into manipulation planning. Departing from the conventional paradigm of seeking a single optimal solution, the proposed method searches for diverse yet robust grasp configurations and generates multimodal manipulation trajectories to construct a repertoire of resilient policies. This approach significantly enhances behavioral diversity and dynamic adaptability during manipulation tasks. By yielding a rich set of viable strategies rather than a solitary plan, the framework equips robotic systems to handle unforeseen variations effectively. Ultimately, this work provides a novel pathway toward the reliable deployment of robots in continuously evolving, open-world settings where environmental uncertainty demands flexible and robust operational capabilities.

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.

Problem

• Prehensile Manipulation
• Open-Ended Environments
• Robust Grasping
• Articulated Objects
• Generalization

Innovation

• Quality-Diversity algorithms
• Prehensile manipulation
• Robust grasp configurations
• Articulated objects
• Open-ended environments