Triple-S: A Collaborative Multi-LLM Framework for Solving Long-Horizon Implicative Tasks in Robotics

2025-08-10

AI Summary

LLMs frequently misconfigure API parameters, omit critical annotations, and violate execution ordering constraints when executing long-horizon implicit robotic tasks. To address these challenges, this paper proposes a multi-LLM collaborative closed-loop framework. Our method introduces: (1) a role-based, three-stage collaboration mechanism—Simplification (task decomposition and understanding), Solution (code generation), and Summary (result abstraction)—to decouple and specialize each functional phase; and (2) a success-driven dynamic demonstration library that enables generalized repair of failed tasks via context-aware, real-time demonstration updates. Integrating in-context learning with adaptive demonstration retrieval, our approach achieves 89% task success on the LDIP benchmark across both fully and partially observable settings. Extensive validation confirms robust performance in both simulation and real-world robotic platforms.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Leveraging Large Language Models (LLMs) to write policy code for controlling robots has gained significant attention. However, in long-horizon implicative tasks, this approach often results in API parameter, comments and sequencing errors, leading to task failure. To address this problem, we propose a collaborative Triple-S framework that involves multiple LLMs. Through In-Context Learning, different LLMs assume specific roles in a closed-loop Simplification-Solution-Summary process, effectively improving success rates and robustness in long-horizon implicative tasks. Additionally, a novel demonstration library update mechanism which learned from success allows it to generalize to previously failed tasks. We validate the framework in the Long-horizon Desktop Implicative Placement (LDIP) dataset across various baseline models, where Triple-S successfully executes 89% of tasks in both observable and partially observable scenarios. Experiments in both simulation and real-world robot settings further validated the effectiveness of Triple-S. Our code and dataset is available at: https://github.com/Ghbbbbb/Triple-S.

Problem

• Addresses API and sequencing errors in robot policy code
• Improves success rates in long-horizon implicative robotics tasks
• Enhances robustness through multi-LLM collaboration and learning

Innovation

• Collaborative multi-LLM framework for robotics
• Simplification-Solution-Summary closed-loop process
• Demonstration library update from success