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Accessibility is one of the most important features in the design of robotsand their interfaces. This thesis proposes methods that improve theaccessibility of robots for three different target audiences: consumers,researchers, and learners. In order for humans and robots to work togethereffectively, they both must be able to communicate with each other. We tacklethe problem of generating route instructions that are readily understandable bynovice humans for the navigation of a priori unknown indoor environments. Wethen move on to the related problem of enabling robots to understand naturallanguage utterances in the context of learning to operate articulated objects(e.g., fridges, drawers) by leveraging kinematic models. Next, we turn ourfocus to the development of accessible and reproducible robotic platforms forscientific research. We propose a new concept for reproducible roboticsresearch that integrates development and benchmarking, so that reproducibilityis obtained "by design" from the beginning of the research and developmentprocess. We then propose a framework called SHARC (SHared Autonomy for RemoteCollaboration), to improve accessibility for underwater robotic interventionoperations. SHARC allows multiple remote scientists to efficiently plan andexecute high-level sampling procedures using an underwater manipulator whiledeferring low-level control to the robot. Lastly, we developed the firsthardware-based MOOC in AI and robotics. This course allows learners to studyautonomy hands-on by making real robots make their own decisions and accomplishbroadly defined tasks. We design a new robotic platform from the ground up tosupport this new learning experience. A fully browser-based interface, based onleading tools and technologies for code development, testing, validation, anddeployment serves to maximize the accessibility of these educational resources.

Accessible Interfaces for the Development and Deployment of Robotic Platforms