Robots are experiencing a revolution as they permeate many aspects of our daily lives, from performing house maintenance to infrastructure inspection, from efficiently warehousing goods to autonomous vehicles, and more. This technical progress and its impact are astounding. This revolution, however, is outstripping the capabilities of existing software development processes, techniques, and tools, which largely have remained unchanged for decades. These capabilities are ill-suited to handling the challenges unique to robotics software such as dealing with a wide diversity of domains, heterogeneous hardware, programmed and learned components, complex physical environments captured and modeled with uncertainty, emergent behaviors that include human interactions, and scalability demands that span across multiple dimensions. Looking ahead to the need to develop software for robots that are ever more ubiquitous, autonomous, and reliant on complex adaptive components, hardware, and data, motivated an NSF-sponsored community workshop on the subject of Software Engineering for Robotics, held in Detroit, Michigan in October 2023. The goal of the workshop was to bring together thought leaders across robotics and software engineering to coalesce a community, and identify key problems in the area of SE for robotics that that community should aim to solve over the next 5 years. This report serves to summarize the motivation, activities, and findings of that workshop, in particular by articulating the challenges unique to robot software, and identifying a vision for fruitful near-term research directions to tackle them.

翻译：机器人正经历一场革命，它们渗透到日常生活的方方面面：从家务维护到基础设施巡检，从高效仓储到自动驾驶，不一而足。这一技术进展及其影响令人惊叹。然而，这场革命正超越现有软件开发流程、技术和工具的能力——这些能力数十年来基本未有改变。它们难以应对机器人软件独有的挑战，例如：处理广泛多样的领域、异构硬件、编程与学习组件、以不确定性捕获与建模的复杂物理环境、包含人机交互的涌现行为，以及横跨多个维度的可扩展性需求。展望未来，需要为日益普及、自主化、依赖于复杂自适应组件、硬件和数据的机器人开发软件，这一需求促使美国国家科学基金会（NSF）于2023年10月在密歇根州底特律举办了以“机器人软件工程”为主题的社区研讨会。研讨会的目标是汇聚机器人与软件工程领域的学术领袖，凝聚社区共识，并确定该领域在未来五年内应着力解决的关键问题。本报告旨在总结该研讨会的动机、活动与成果，尤其通过阐明机器人软件独有的挑战，并描绘应对这些挑战的富有成效的近期研究方向愿景。