Machine learning has significantly enhanced the abilities of robots, enabling them to perform a wide range of tasks in human environments and adapt to our uncertain real world. Recent works in various machine learning domains have highlighted the importance of accounting for fairness to ensure that these algorithms do not reproduce human biases and consequently lead to discriminatory outcomes. With robot learning systems increasingly performing more and more tasks in our everyday lives, it is crucial to understand the influence of such biases to prevent unintended behavior toward certain groups of people. In this work, we present the first survey on fairness in robot learning from an interdisciplinary perspective spanning technical, ethical, and legal challenges. We propose a taxonomy for sources of bias and the resulting types of discrimination due to them. Using examples from different robot learning domains, we examine scenarios of unfair outcomes and strategies to mitigate them. We present early advances in the field by covering different fairness definitions, ethical and legal considerations, and methods for fair robot learning. With this work, we aim to pave the road for groundbreaking developments in fair robot learning.

翻译：机器学习显著增强了机器人的能力，使其能够在人类环境中执行各种任务并适应我们不确定的现实世界。近期多个机器学习领域的研究强调了考虑公平性的重要性，以确保这些算法不会复制人类偏见，从而导致歧视性结果。随着机器人学习系统在我们日常生活中执行的任务越来越多，理解此类偏见的影响以防止对特定人群产生非预期行为至关重要。本工作首次从技术、伦理与法律挑战的跨学科视角，对机器人学习中的公平性进行了综述研究。我们提出了一种分类体系，用于界定偏见的来源及其导致的歧视类型。通过不同机器人学习领域的实例，我们分析了不公平结果的情景及其缓解策略。本文涵盖了不同的公平性定义、伦理与法律考量，以及公平机器人学习的方法，总结了该领域的早期进展。本工作旨在为公平机器人学习的突破性发展铺平道路。