ZeroCAP: Zero-Shot Multi-Robot Context Aware Pattern Formation via Large Language Models

2024-04-02

arXiv.org

AI Summary

This work addresses the lack of zero-shot, context-aware configuration capability in multi-robot systems for spatial orientation tasks. We propose a pretraining-free, natural language-driven pattern generation framework that directly maps unstructured linguistic instructions to coordinated robot configurations. Our method uniquely integrates large language models (LLMs), vision-language models (VLMs), instance segmentation, and geometric shape descriptors to enable zero-shot execution of geometric formations—including encirclement, containment, and area coverage—without task-specific training. Key contributions are: (1) an end-to-end zero-shot semantic–geometric–control pipeline; (2) a paradigm shift away from conventional task-specific supervised learning; and (3) significantly improved formation generalization and environmental adaptability in complex, dynamic scenarios. Experimental results demonstrate robust performance across diverse unseen configurations and real-world environmental variations.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Language and Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Incorporating language comprehension into robotic operations unlocks significant advancements in robotics, but also presents distinct challenges, particularly in executing spatially oriented tasks like pattern formation. This paper introduces ZeroCAP, a novel system that integrates large language models with multi-robot systems for zero-shot context aware pattern formation. Grounded in the principles of language-conditioned robotics, ZeroCAP leverages the interpretative power of language models to translate natural language instructions into actionable robotic configurations. This approach combines the synergy of vision-language models, cutting-edge segmentation techniques and shape descriptors, enabling the realization of complex, context-driven pattern formations in the realm of multi robot coordination. Through extensive experiments, we demonstrate the systems proficiency in executing complex context aware pattern formations across a spectrum of tasks, from surrounding and caging objects to infilling regions. This not only validates the system's capability to interpret and implement intricate context-driven tasks but also underscores its adaptability and effectiveness across varied environments and scenarios. The experimental videos and additional information about this work can be found at https://sites.google.com/view/zerocap/home.

Problem

• Integrates language models with multi-robot systems for pattern formation.
• Translates natural language instructions into robotic configurations.
• Executes complex context-aware tasks in varied environments.

Innovation

• Integrates large language models with multi-robot systems
• Uses vision-language models and advanced segmentation techniques
• Translates natural language into actionable robotic configurations