Executable Analytic Concepts as the Missing Link Between VLM Insight and Precise Manipulation

2025-10-09

AI Summary

This work addresses the semantic–physical gap between high-level visual-language understanding and low-level robotic manipulation, enabling zero-shot generalization to novel tasks in unstructured environments without task-specific training. Method: We propose Executable Abstraction Concepts (EACs), a unified mathematical formalism that jointly encodes object functionality, geometric constraints, and operational semantics—thereby establishing an interpretable, end-to-end mapping from language instructions to executable robot actions. Our framework integrates vision-language models (VLMs), natural language processing (NLP), geometric reasoning, and motion planning to generate grasp poses, force directions, and dynamically feasible trajectories. Contribution/Results: Evaluated in both simulation and real-world settings, our approach achieves strong zero-shot generalization across diverse articulated objects under complex natural-language commands, significantly improving manipulation accuracy and environmental adaptability compared to prior methods.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Language and Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Bridging semantic reasoning to precise physical robot execution
• Enabling zero-shot generalization for manipulation without task-specific training
• Translating natural language instructions into executable geometric constraints

Innovation

• GRACE framework bridges semantic reasoning with physical execution
• Executable analytic concepts encode object affordances and constraints
• Structured policy scaffolding converts instructions into robot trajectories