Multimodal system aligns robot actions with human goals using language
From Language to Drives: A Multimodal Framework for Aligning Autonomous Robots with Human Purposes
RoboticsArtificial IntelligenceHuman-Computer Interaction
Summary
Robots that learn on their own often struggle to keep their goals aligned with what humans want them to do. This paper presents a system that uses large language and vision models to help robots understand human intentions expressed in natural language. The system turns these intentions into specific missions and motivational drives for the robot, which guide its behavior. The process includes feedback loops so the robot’s actions stay aligned with human purposes. Experiments show this approach produces valid behaviors in both simulated and real robots.
What this means in practice
- •For robotics developers: Create autonomous robots that interpret human commands into actionable missions using language and vision models for better alignment.
- •For human-robot interaction designers: Design interactive systems where robots adapt behaviors continuously to match evolving human goals expressed in natural language.
- •For smart home device makers: Enable home robots to follow nuanced user instructions by converting natural language into mission-driven actions using language models.$Commercial implications: Makes more intuitive control products feasible by linking language understanding directly to robot action planning.
Authors
Diana Cerdas-Vargas, Alejandro Romero, Richard J. Duro
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.