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Émiland Garrabé

2 accepted papers

2025

Enhancing Robustness in Language-Driven Robotics: A Modular Approach to Failure Reduction

IROS 2025

Recent advances in large language models (LLMs) have led to significant progress in robotics, enabling embodied agents to understand and execute open-ended tasks. However, existing LLM-based approaches face limitations in grounding their outputs within the physical environment and aligning with the

Cited by 3SourceScholar
2025

Task-Aware Robotic Grasping by evaluating Quality Diversity Solutions through Foundation Models

IROS 2025

Task-aware robotic grasping is a challenging problem that requires the integration of semantic understanding and geometric reasoning. This paper proposes a novel framework that leverages Large Language Models (LLMs) and Quality Diversity (QD) algorithms to enable zero-shot task-conditioned grasp syn

Cited by 1SourceScholar