Executable Analytic Concepts as the Missing Link Between VLM Insight and Precise Manipulation
This addresses the problem of enabling robots to perform precise and generalized manipulation in unstructured environments for embodied AI, representing a novel method rather than an incremental improvement.
The paper tackles the gap between high-level semantic understanding from Vision-Language Models and precise physical execution in robot manipulation by introducing GRACE, a framework that uses executable analytic concepts to ground reasoning, achieving strong zero-shot generalization across articulated objects in simulated and real-world environments without task-specific training.
Enabling robots to perform precise and generalized manipulation in unstructured environments remains a fundamental challenge in embodied AI. While Vision-Language Models (VLMs) have demonstrated remarkable capabilities in semantic reasoning and task planning, a significant gap persists between their high-level understanding and the precise physical execution required for real-world manipulation. To bridge this "semantic-to-physical" gap, we introduce GRACE, a novel framework that grounds VLM-based reasoning through executable analytic concepts (EAC)-mathematically defined blueprints that encode object affordances, geometric constraints, and semantics of manipulation. Our approach integrates a structured policy scaffolding pipeline that turn natural language instructions and visual information into an instantiated EAC, from which we derive grasp poses, force directions and plan physically feasible motion trajectory for robot execution. GRACE thus provides a unified and interpretable interface between high-level instruction understanding and low-level robot control, effectively enabling precise and generalizable manipulation through semantic-physical grounding. Extensive experiments demonstrate that GRACE achieves strong zero-shot generalization across a variety of articulated objects in both simulated and real-world environments, without requiring task-specific training.