基于Hyperledger Fabric的去中心化意图多机器人任务规划器与LLM预言机
Decentralized Intent-Based Multi-Robot Task Planner with LLM Oracles on Hyperledger Fabric
AI总结：
本文提出基于Hyperledger Fabric的去中心化多机器人任务规划器，结合LLM预言机与新聚合方法，实现意图分解与多机器人协调。
摘要：
大型语言模型（LLM）为将自然语言用户意图转换为可执行动作开辟了新机会。这种能力使具身AI代理能够执行复杂任务，而无需专家参与，使人机交互（HRI）更加便捷。然而，这些发展带来了显著的安全性和隐私挑战，例如自我偏好，即单一LLM服务提供商主导市场并利用其权力推广自身偏好。LLM预言机最近被提出作为一种机制，通过执行不同供应商的多个LLM并聚合其输出来去中心化LLM。然而，这些方法的准确性高度依赖于聚合方法。当前的聚合方法大多使用不同LLM输出之间的语义相似性，这不适合机器人任务规划，因为任务的时序顺序很重要。为填补这一空白，我们提出了一种针对机器人任务规划的新聚合方法的LLM预言机。此外，我们还提出了一种基于Hyperledger Fabric的去中心化多机器人基础设施，可以托管所提出的预言机。该基础设施使用户能够向系统表达自然语言意图，然后将其分解为子任务。这些子任务需要协调不同供应商的机器人，同时对数据实施细粒度的访问控制管理。为了评估我们的方法，我们创建了SkillChain-RTD基准测试，并将其公开。我们的实验结果证明了所提出架构的可行性，并且所提出的聚合方法优于当前使用的其他聚合方法。
英文摘要：
Large language models (LLMs) have opened new opportunities for transforming natural language user intents into executable actions. This capability enables embodied AI agents to perform complex tasks, without involvement of an expert, making human-robot interaction (HRI) more convenient. However these developments raise significant security and privacy challenges such as self-preferencing, where a single LLM service provider dominates the market and uses this power to promote their own preferences. LLM oracles have been recently proposed as a mechanism to decentralize LLMs by executing multiple LLMs from different vendors and aggregating their outputs to obtain a more reliable and trustworthy final result. However, the accuracy of these approaches highly depends on the aggregation method. The current aggregation methods mostly use semantic similarity between various LLM outputs, not suitable for robotic task planning, where the temporal order of tasks is important. To fill the gap, we propose an LLM oracle with a new aggregation method for robotic task planning. In addition, we propose a decentralized multi-robot infrastructure based on Hyperledger Fabric that can host the proposed oracle. The proposed infrastructure enables users to express their natural language intent to the system, which then can be decomposed into subtasks. These subtasks require coordinating different robots from different vendors, while enforcing fine-grained access control management on the data. To evaluate our methodology, we created the SkillChain-RTD benchmark made it publicly available. Our experimental results demonstrate the feasibility of the proposed architecture, and the proposed aggregation method outperforms other aggregation methods currently in use.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
