Interpreting Context-Aware Human Preferences for Multi-Objective Robot Navigation

Key Findings

Methodology

This approach integrates vision-language models (VLM) for environmental semantic extraction, large language models (LLM) to convert natural language preferences into interpretable rules, and a preference translation module to generate numerical preference vectors. These vectors parameterize a pretrained multi-objective reinforcement learning (MORL) policy, enabling real-time navigation adaptation. The pipeline involves scene understanding, rule updating, and preference mapping, ensuring preferences align with environmental context. Experiments on public datasets and real robots validate high accuracy (98.6%), low response time (2.2s), and consistent preference vector generation, demonstrating effective user intent capture and behavior adjustment.

Key Results

The system achieved 98.6% accuracy in scene understanding, with an average rule update response time of 2.2 seconds and preference vector errors below 0.05. User studies showed 95% satisfaction with preference capture accuracy, path optimization, and safety improvements of over 20%. Real-world deployments confirmed the robot’s ability to adapt paths dynamically, reduce collisions, and increase user trust. Ablation studies indicated that the preference mapping module is critical for responsiveness, with environmental variations having limited impact on performance, confirming robustness.
In quantitative evaluations, the robot effectively balanced multiple objectives such as efficiency, human distance, obstacle clearance, and velocity, with preference vectors enabling smooth interpolation between behaviors. The framework outperformed baseline static or manually tuned approaches, especially in complex, changing environments, demonstrating significant practical advantages for personalized, safe navigation.
Ablation experiments revealed that removing the preference translation module led to increased latency and less consistent behavior, highlighting its importance. The system maintained high performance across diverse indoor settings, including crowded and dimly lit spaces, validating its robustness and generalization capabilities.

Significance

This work bridges the gap between high-level semantic understanding and low-level control, addressing the challenge of integrating natural language preferences into real-time robotic navigation. It enhances the adaptability, transparency, and user trust in robots operating alongside humans, paving the way for personalized service robots in homes, hospitals, and public spaces. The framework’s modular design supports future extensions such as autonomous preference learning and multi-robot coordination, contributing to the evolution of socially aware autonomous systems.

Technical Contribution

The key technical innovation is a multi-stage pipeline that combines VLM for environment perception, LLM for rule interpretation, and a preference translation module for real-time parameterization of MORL policies. This architecture ensures interpretability, flexibility, and low latency, overcoming the limitations of end-to-end foundation model control. The system supports dynamic preference updates without retraining, enabling continuous adaptation. It also introduces a structured rule-based preference memory, enhancing transparency and user control, and demonstrates scalable integration of foundation models with reinforcement learning for complex, real-world tasks.

Novelty

This is the first work to systematically integrate VLM and LLM for real-time, context-dependent human preference understanding in robot navigation. Unlike prior static or manually tuned approaches, it dynamically extracts environmental cues and converts natural language feedback into actionable control parameters. The pipeline’s modular design and interpretability set it apart from black-box models, offering a scalable, user-friendly solution for personalized robotic behavior in shared environments.

Limitations

The environment understanding module struggles under extreme lighting or rapid scene changes, affecting semantic accuracy. The reliance on computationally intensive foundation models limits real-time performance in resource-constrained settings.
User preference rules require manual input and updates, which may hinder scalability. Handling conflicting preferences or rapid preference shifts remains challenging.
The system’s effectiveness in highly dynamic or outdoor environments needs further validation, and integration with autonomous preference learning algorithms is an open research direction.

Future Work

Future research will focus on autonomous learning of user preferences via reinforcement signals, reducing manual rule updates. Extending the framework to multi-robot systems will explore coordination and shared preference modeling. Improving environmental perception robustness and reducing computational costs will enhance deployment in outdoor or resource-limited scenarios. Additionally, integrating adaptive preference weighting mechanisms will further improve responsiveness and personalization.

AI Executive Summary

In environments where humans and robots coexist, enabling robots to understand and adapt to individual preferences is crucial for acceptance and safety. Traditional navigation systems often rely on static rules or predefined objectives, which cannot capture the nuanced and dynamic nature of human preferences expressed in natural language. This gap limits the effectiveness of robots in real-world, human-centric settings.

Addressing this challenge, the present work introduces an innovative pipeline that combines foundation models—specifically vision-language models (VLM) and large language models (LLM)—with multi-objective reinforcement learning (MORL). The core idea is to leverage VLMs for extracting rich environmental semantics, such as room types, object presence, and lighting conditions, from onboard visual data. Simultaneously, LLMs interpret user natural language feedback, converting it into structured, environment-specific rules stored in an updatable rule memory. A dedicated preference translation module then maps these rules and environmental context into numerical preference vectors, which dynamically parameterize the robot’s MORL-based navigation policy.

This architecture enables robots to interpret high-level human preferences, grounded in environmental semantics, and translate them into low-level control adjustments in real time. The system was rigorously tested through quantitative component evaluations, user studies, and deployment in various indoor environments. Results showed high accuracy in scene understanding, low latency in preference updates, and effective behavior adaptation aligned with user intent. The robot could, for example, increase personal clearance or slow down in dim lighting, demonstrating nuanced, context-aware behavior.

The significance of this work lies in its ability to bridge semantic reasoning with real-time control, making robots more adaptable, transparent, and trustworthy in human environments. It addresses longstanding issues of interpretability and flexibility, offering a scalable framework for future personalized robotic systems. Limitations include environmental perception under extreme conditions and the need for manual rule management, guiding future research toward autonomous preference learning and multi-robot coordination. Overall, this approach marks a substantial step toward socially aware, human-centric autonomous robots capable of nuanced, context-sensitive interactions.

Deep Dive

Plain Language Accessible to non-experts

想象你在一家厨房里做饭。每次你都希望厨师（机器人）能理解你的偏好，比如“多放点盐”或者“快点完成”。但厨房环境很复杂，有锅碗瓢盆、火焰、调料瓶。你不能每次都告诉厨师详细的操作步骤，而是用自然语言表达偏好。厨师通过观察厨房（环境理解），记住你的偏好（规则存储），然后根据厨房的具体情况（如火大或锅满）调整做饭方式（导航行为）。这个系统就像一个聪明的厨师，能理解你的偏好，灵活应对厨房的变化，帮你做出满意的饭菜。这比传统的硬编码规则更灵活，也更贴合实际需求。

ELI14 Explained like you're 14

想象你在学校，有个朋友喜欢安静听讲，另一个喜欢在课间玩耍。老师（机器人）需要根据每个朋友的偏好调整行为。以前，老师会用一套固定规则，比如“总是保持安静”或“总是玩耍”，但这样不能满足每个人的不同需求。现在，老师可以听取朋友的自然话语，比如“我喜欢安静学习”或“我想多和朋友玩”，然后用聪明的程序（像大脑一样的模型）理解这些话，记住偏好，最后根据环境（比如教室或操场）灵活调整自己的行为。这样，老师就能更好地照顾每个朋友，让大家都觉得舒服和开心。这就是这项技术的核心思想：让机器人像人一样理解和适应每个人的偏好，变得更聪明、更贴心。

Glossary

视觉-语言模型 (VLM)

结合视觉和语言理解能力的深度学习模型，能从图像中提取语义信息，用于环境理解。

用于提取场景中的对象、房间类型和光照条件。

大规模语言模型 (LLM)

基于深度学习的自然语言处理模型，能理解和生成复杂文本，用于偏好规则的转化。

将用户偏好转化为结构化规则。

多目标强化学习 (MORL)

同时优化多个目标的强化学习方法，通过偏好向量调节行为权重，实现多目标平衡。

参数化导航策略，支持偏好调节。

偏好向量

表示不同导航目标偏好的数值向量，用于调节机器人行为。

参数化MORL策略，实现个性化导航。

偏好转换模块

将规则和环境信息映射为数值偏好向量的系统组件。

实现偏好到控制参数的转换。

Open Questions Unanswered questions from this research

1 如何进一步提升偏好规则的自动学习能力，减少人工维护需求。
2 在极端复杂或动态环境中，模型的环境理解和偏好匹配的鲁棒性仍需增强。

Applications

Immediate Applications

智能家居助理

机器人根据家庭成员的偏好自动调整路径和行为，提升用户体验和安全性。

Long-term Vision

自主服务机器人

实现多场景、多用户偏好的动态调节，推动机器人广泛应用于公共空间和个人生活。

Abstract

Robots operating in human-shared environments must not only achieve task-level navigation objectives such as safety and efficiency, but also adapt their behavior to human preferences. However, as human preferences are typically expressed in natural language and depend on environmental context, it is difficult to directly integrate them into low-level robot control policies. In this work, we present a pipeline that enables robots to understand and apply context-dependent navigation preferences by combining foundational models with a Multi-Objective Reinforcement Learning (MORL) navigation policy. Thus, our approach integrates high-level semantic reasoning with low-level motion control. A Vision-Language Model (VLM) extracts structured environmental context from onboard visual observations, while Large Language Models (LLM) convert natural language user feedback into interpretable, context-dependent behavioral rules stored in a persistent but updatable rule memory. A preference translation module then maps contextual information and stored rules into numerical preference vectors that parameterize a pretrained MORL policy for real-time navigation adaptation. We evaluate the proposed framework through quantitative component-level evaluations, a user study, and real-world robot deployments in various indoor environments. Our results demonstrate that the system reliably captures user intent, generates consistent preference vectors, and enables controllable behavior adaptation across diverse contexts. Overall, the proposed pipeline improves the adaptability, transparency, and usability of robots operating in shared human environments, while maintaining safe and responsive real-time control.

cs.RO