SACSoN：面向社交导航的可扩展自主控制
SACSoN: Scalable Autonomous Control for Social Navigation
AI总结：
本文提出SACSoN方法，通过最小化对人类行为的反事实扰动训练社交导航策略，并公开了同类最大规模的室内人机互动视觉导航数据集。
摘要：
机器学习为构建超越简单人类行为预测模型的社交合规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互动，学习可以直接从数据中实现有效的社交导航行为。本文的目标是开发训练社交不扰人导航策略的方法，使机器人能够在人群中以不干扰人类行为的方式导航。我们基于对人类的反事实扰动引入此类行为的定义：如果机器人没有侵入该空间，人类是否会以相同方式行动？通过最小化这种反事实扰动，我们可以引导机器人在共享空间中表现出不改变人类自然行为的方式。实例化这一原则需要训练策略以最小化其对人类行为的影响，而这反过来又需要能够让我们对机器人在场时人类行为进行建模的数据。因此，我们的方法基于两项关键贡献。第一，我们收集了一个室内移动机器人与人类旁观者互动的大型数据集。第二，我们利用该数据集训练最小化反事实扰动的策略。我们提供了补充视频，并在项目页面上公开了同类中规模最大的视觉导航数据集。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
