Accessibility is one of the most important features in the design of robots and their interfaces. This thesis proposes methods that improve the accessibility of robots for three different target audiences: consumers, researchers, and learners. In order for humans and robots to work together effectively, they both must be able to communicate with each other. We tackle the problem of generating route instructions that are readily understandable by novice humans for the navigation of a priori unknown indoor environments. We then move on to the related problem of enabling robots to understand natural language utterances in the context of learning to operate articulated objects (e.g., fridges, drawers) by leveraging kinematic models. Next, we turn our focus to the development of accessible and reproducible robotic platforms for scientific research. We propose a new concept for reproducible robotics research that integrates development and benchmarking, so that reproducibility is obtained "by design" from the beginning of the research and development process. We then propose a framework called SHARC (SHared Autonomy for Remote Collaboration), to improve accessibility for underwater robotic intervention operations. SHARC allows multiple remote scientists to efficiently plan and execute high-level sampling procedures using an underwater manipulator while deferring low-level control to the robot. Lastly, we developed the first hardware-based MOOC in AI and robotics. This course allows learners to study autonomy hands-on by making real robots make their own decisions and accomplish broadly defined tasks. We design a new robotic platform from the ground up to support this new learning experience. A fully browser-based interface, based on leading tools and technologies for code development, testing, validation, and deployment serves to maximize the accessibility of these educational resources.

翻译：可访问性是机器人及其接口设计中最关键的特征之一。本论文提出了一系列方法，旨在提升机器人对三类不同目标受众（消费者、研究人员及学习者）的可访问性。为实现人机高效协作，双方必须能够相互沟通。我们首先解决了为初次使用者生成易于理解的室内未知环境导航路线指令的问题。随后，我们转向使机器人能够理解自然语言表达的相关挑战——具体而言，通过利用运动学模型，机器人可学习操作铰接式物体（如冰箱、抽屉）。接下来，我们将重点转向开发可用于科学研究的可访问且可复现的机器人平台。我们提出了一种新的可复现机器人研究概念，将开发与基准测试相整合，从而在研究开发初期便"通过设计"确保可复现性。在此基础上，我们提出了一个名为SHARC（共享自主远程协作）的框架，用于提升水下机器人干预操作的可访问性。SHARC允许多位远程科学家高效规划并执行使用水下机械臂的高级采样流程，同时将低级控制权委托给机器人。最后，我们开发了人工智能与机器人学领域首个基于硬件的慕课课程。该课程通过让真实机器人自主决策并完成广义任务，使学习者能够亲手实践自主控制技术。我们从头设计了一个全新的机器人平台来支撑这种新型学习体验，并基于业界领先的代码开发、测试、验证及部署工具与技术，构建了完全基于浏览器的界面，从而最大化这些教育资源的可访问性。