从语言到驱动力：用大语言模型对齐自主机器人与人类目标的多模态框架
From Language to Drives: A Multimodal Framework for Aligning Autonomous Robots with Human Purposes
AI 导读
arXiv 论文提出借助大语言模型（LLM）和视觉语言模型（VLM）的多模态框架，将人类以自然语言表达的目标转化为机器人架构中的任务（missions）和驱动力（drives），以对齐开放式学习自主机器人的行为。系统分语义对齐、任务生成、驱动力建模三层，形成人机双向交互闭环。仿真和实体机器人实验验证了该流程可产生有效任务与行为。
正文 · 原文
Abstract:This work explores a multimodal approach to the alignment of the behavior of open-ended learning autonomous robots with human purposes through the use of large language models as cultural enablers. While autonomous robots can generate their own skills and goals through interaction with the environment, they often lack mechanisms to ensure that these remain relevant and aligned to those of human users. The proposed approach makes use of the general and cultural knowledge contained in LLMs and VLMs to establish a multimodal interactive alignment process in which human purposes, expressed in natural language, are transformed into motivational components of a robot architecture in the form of robot missions and drives that guide the behavior of the robot. The system is structured into three layers for semantic alignment, mission generation, and drive modeling. This design enables a bidirectional interaction loop: human inputs define high-level purposes, robots expand them into executable missions making use of multimodal information relayed by LLMs and VLMs, and feedback maintains alignment with human intent. Experiments in simulation and with a physical robot demonstrate that the process produces valid missions and behaviors, supporting effective human-agent alignment in interactive scenarios with autonomous robots.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2610.35629 [cs.RO] |
| (or arXiv:2610.35629v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35629 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Alejandro Romero [view email]
[v1]
Mon, 26 Oct 2026 15:40:38 UTC (367 KB)
来源：arXiv cs.AI · arxiv.org