Machine learning provides a powerful tool for building socially compliant robotic systems that go beyond simple predictive models of human behavior. By observing and understanding human interactions from past experiences, learning can enable effective social navigation behaviors directly from data. In this paper, our goal is to develop methods for training policies for socially unobtrusive navigation, such that robots can navigate among humans in ways that don't disturb human behavior. We introduce a definition for such behavior based on the counterfactual perturbation of the human: if the robot had not intruded into the space, would the human have acted in the same way? By minimizing this counterfactual perturbation, we can induce robots to behave in ways that do not alter the natural behavior of humans in the shared space. Instantiating this principle requires training policies to minimize their effect on human behavior, and this in turn requires data that allows us to model the behavior of humans in the presence of robots. Therefore, our approach is based on two key contributions. First, we collect a large dataset where an indoor mobile robot interacts with human bystanders. Second, we utilize this dataset to train policies that minimize counterfactual perturbation. We provide supplementary videos and make publicly available the largest-of-its-kind visual navigation dataset on our project page.

翻译：机器学习为构建超越简单人类行为预测模型的社会兼容机器人系统提供了强大工具。通过从过往经验中观察和理解人类交互，学习能够直接从数据中实现有效的社交导航行为。本文旨在开发用于训练社会无扰导航策略的方法，使机器人能在不干扰人类行为的前提下于人群中穿行。我们基于人类的反事实扰动提出此类行为的定义：若机器人未侵入空间，人类是否会以相同方式行动？通过最小化这种反事实扰动，可引导机器人以不改变共享空间中人类自然行为的方式运作。实现该原则需要训练策略以最小化其对人类行为的影响，这反过来需要能够模拟机器人在场时人类行为的数据。因此，我们的方法基于两项核心贡献：首先，我们收集了一个大型数据集，其中室内移动机器人与人类旁观者进行交互；其次，我们利用该数据集训练最小化反事实扰动的策略。我们在项目页面上提供补充视频，并公开规模最大的同类视觉导航数据集。