A robot was programmed to progressively learn appropriate social autonomous behavior from in situ human demonstrations and guidance. Striking the right balance between robot autonomy and human control is a core challenge in social robotics, in both technical and ethical terms. On the one hand, extended robot autonomy offers the potential for increased human productivity and for the off-loading of physical and cognitive tasks. On the other hand, making the most of human technical and social expertise, as well as maintaining accountability, is highly desirable. This is particularly relevant in domains such as medical therapy and education, where social robots hold substantial promise, but where there is a high cost to poorly performing autonomous systems, compounded by ethical concerns. We present a field study in which we evaluate SPARC (supervised progressively autonomous robot competencies), an innovative approach addressing this challenge whereby a robot progressively learns appropriate autonomous behavior from in situ human demonstrations and guidance. Using online machine learning techniques, we demonstrate that the robot could effectively acquire legible and congruent social policies in a high-dimensional child-tutoring situation needing only a limited number of demonstrations while preserving human supervision whenever desirable. By exploiting human expertise, our technique enables rapid learning of autonomous social and domain-specific policies in complex and nondeterministic environments. Last, we underline the generic properties of SPARC and discuss how this paradigm is relevant to a broad range of difficult human-robot interaction scenarios.

中文翻译：

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从原地人类指导中教机器人社会自治

对机器人进行了编程，以从原地人类演示和指导中逐步学习适当的社交自主行为。无论从技术还是从伦理上来讲，在机器人自主性和人为控制之间寻求适当的平衡都是社会机器人技术的一项核心挑战。一方面，扩展的机器人自主性为提高人类生产力以及减轻身体和认知任务的负担提供了潜力。另一方面，非常需要充分利用人类的技术和社会专业知识，以及保持问责制。这在医学治疗和教育等领域尤其重要，在这些领域中，社交机器人具有巨大的希望，但在表现不佳的自治系统中，由于道德方面的考虑，成本很高。我们提出了一项实地研究，其中我们评估了SPARC（监督的渐进式自主机器人能力），这是一种应对这一挑战的创新方法，机器人可以从原地的人类演示和指导中逐步学习适当的自主行为。通过使用在线机器学习技术，我们证明了该机器人可以在仅需少量演示的情况下，在高维度的儿童辅导情况下有效地获取清晰且一致的社会政策，并在需要时保持人工监督。通过利用人类的专业知识，我们的技术可以在复杂且不确定的环境中快速学习自治的社会策略和特定于领域的策略。最后的，