让机器人导航更懂人类意图，避免行为偏差。
Aligning Robot Navigation Behaviors with Human Intentions and Preferences
- 通过模仿人类示范学习导航目标，优化对齐人类意图的损失函数。
- 自监督学习地形感知，使机器人能适应新环境并尊重用户偏好。
- 构建社会合规导航数据集与算法，适用于室内外人机共存场景。
近年来机器学习的发展为移动机器人提供了先进的导航能力，但基于学习的方法可能导致机器人行为与人类意图和偏好不一致，即价值错位问题。本论文旨在解决：如何利用机器学习方法使自主移动机器人的导航行为与人类意图和偏好对齐？首先，提出一种新方法，通过模仿人类提供的导航任务示范来学习导航行为，采用新型目标函数，鼓励智能体与人类导航目标对齐，并惩罚偏离。其次，提出两种算法，通过自监督方式学习视觉地形感知，使机器人在城市户外环境中尊重操作者对不同地形的偏好，并能通过多模态表示将这些偏好推广至视觉上新颖的地形。最后，在有人类活动的环境中，构建了一个数据集和算法，实现机器人在室内外环境中社会合规的导航。总体而言，本论文在解决自主导航中的价值对齐问题上迈出关键一步，使移动机器人能够以符合人类意图和偏好的目标自主导航。
原文摘要 · Abstract (English)
Recent advances in the field of machine learning have led to new ways for mobile robots to acquire advanced navigational capabilities. However, these learning-based methods raise the possibility that learned navigation behaviors may not align with the intentions and preferences of people, a problem known as value misalignment. To mitigate this risk, this dissertation aims to answer the question: "How can we use machine learning methods to align the navigational behaviors of autonomous mobile robots with human intentions and preferences?" First, this dissertation addresses this question by introducing a new approach to learning navigation behaviors by imitating human-provided demonstrations of the intended navigation task. This contribution allows mobile robots to acquire autonomous visual navigation capabilities through imitation, using a novel objective function that encourages the agent to align with the human's navigation objectives and penalizes misalignment. Second, this dissertation introduces two algorithms to enhance terrain-aware off-road navigation for mobile robots by learning visual terrain awareness in a self-supervised manner. This contribution enables mobile robots to respect a human operator's preferences for navigating different terrains in urban outdoor environments, while extrapolating these preferences to visually novel terrains by leveraging multi-modal representations. Finally, in the context of robot navigation in human-occupied environments, this dissertation introduces a dataset and an algorithm for robot navigation in a socially compliant manner in both indoor and outdoor environments. In summary, the contributions in this dissertation take significant steps toward addressing the value alignment problem in autonomous navigation, enabling mobile robots to navigate autonomously with objectives that align with human intentions and preferences.
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