Bridging Semantics and Kinematics: A Modular Framework for Zero-Shot Robotic Manipulation
For robotic manipulation in semi-structured environments, this framework enables zero-shot execution of complex physical actions from unconstrained language commands, though the 62% success rate indicates room for improvement.
This work introduces a training-free modular framework for zero-shot language-guided robotic manipulation, achieving 62% end-to-end task success across open-world sequential manipulation and dense relational spatial reasoning without domain-specific training.
This paper presents a modular training-free framework for zero-shot, language-guided robotic manipulation in semi-structured environments. The architecture bridges the gap between high-level reasoning and low-level kinematics by decomposing the vision-action pipeline into three stages: visual perception, semantic interpretation, and task execution. To overcome the spatial ambiguity and semantic hallucinations inherent in standard Vision-Language Models (VLMs), the perception module employs FastSAM and Set-of-Mark (SoM) prompting to dynamically generate grounded, alphanumeric visual anchors. The same foundation model then operates purely as a Large Language Model (LLM) to act as a semantic router, translating unconstrained human directives into verifiable, reconfigurable configurations. Finally, these configurations are dynamically parsed by a Task Orchestrator into MoveIt Task Constructor (MTC) to generate collision-free trajectories. The framework is evaluated across two zero-shot experimental setups: unconstrained open-world sequential manipulation and dense relational spatial reasoning, achieving a 62% end-to-end task success rate across both scenarios, demonstrating its capacity to reliably execute complex physical actions without domain-specific training or manual coordinate programming.