Abstract
It is challenging for autonomous control systems to perform complex tasks in the presence of latent risks. Motivated by this challenge, this paper proposes an integrated framework that involves Large Language Models (LLMs), stochastic gradient descent (SGD), and optimization-based control. In the first phrase, the proposed framework breaks down complex tasks into a sequence of smaller subtasks, whose specifications account for contextual information and latent risks. In the second phase, these subtasks and their parameters are refined through a dual process involving LLMs and SGD. LLMs are used to generate rough guesses and failure explanations, and SGD is used to fine-tune parameters. The proposed framework is tested using simulated case studies of robots and vehicles. The experiments demonstrate that the proposed framework can mediate actions based on the context and latent risks and learn complex behaviors efficiently.
Abstract (translated)
对于具有潜在风险的环境中执行复杂任务，自主控制系统具有一定的挑战性。为了应对这一挑战，本文提出了一种集成框架，涉及到大语言模型（LLMs）、随机梯度下降（SGD）和基于优化的控制。在第一段中，所提出的框架将复杂任务分解为一系列较小的子任务，这些子任务的规格考虑了上下文信息和潜在风险。在第二阶段，这些子任务及其参数通过涉及LLMs和SGD的双过程进行进一步优化。LLM用于生成粗略猜测和失败解释，而SGD用于微调参数。所提出的框架通过模拟机器人及车辆的案例研究进行了测试。实验结果表明，与传统方法相比，所提出的框架能够通过上下文及潜在风险来调节行为，并能够有效地学习复杂的行为。
URL
https://arxiv.org/abs/2403.11863
