Towards Robust Prehensile Manipulation in Open-Ended Environments
cs.RO
Submitted: 2026-10-05
Updated: 2026-10-05
Comments: 3 pages, 1 figure. Accepted at the 7th International Workshop on Intrinsically Motivated Open-ended Learning (IMOL 2025)
License: http://creativecommons.org/licenses/by/4.0/
The gist: We propose to use Quality-Diversity (QD) algorithms to solve robotic prehensile manipulation tasks 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.
Sources
- AO-Grasp: Articulated Object Grasp Generation
- Illuminating search spaces by mapping elites
- Quality Diversity under Sparse Reward and Sparse Interaction: Application to Grasping in Robotics
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