Autonomous robots have real-world applications in diverse fields, such as mobile manipulation and environmental exploration, and many such tasks benefit from a hands-off approach in terms of human user involvement over a long task horizon. However, the level of autonomy achievable by a deployment is limited in part by the problem definition or task specification required by the system. Task specifications often require technical, low-level information that is unintuitive to describe and may result in generic solutions, burdening the user technically both before and after task completion. In this thesis, we aim to advance task specification abstraction toward the goal of increasing robot autonomy in real-world scenarios. We do so by tackling problems that address several different angles of this goal. First, we develop a way for the automatic discovery of optimal transition points between subtasks in the context of constrained mobile manipulation, removing the need for the human to hand-specify these in the task specification. We further propose a way to automatically describe constraints on robot motion by using demonstrated data as opposed to manually-defined constraints. Then, within the context of environmental exploration, we propose a flexible task specification framework, requiring just a set of quantiles of interest from the user that allows the robot to directly suggest locations in the environment for the user to study. We next systematically study the effect of including a robot team in the task specification and show that multirobot teams have the ability to improve performance under certain specification conditions, including enabling inter-robot communication. Finally, we propose methods for a communication protocol that autonomously selects useful but limited information to share with the other robots.

翻译：自主机器人在移动操作与环境探索等多个领域具有实际应用价值，此类任务通常需要较长时间跨度内减少人类用户干预。然而，部署机器人时所能实现的自主程度，部分受限于系统所需的问题定义或任务规范。任务规范常需提供技术性强、低层级且描述不直观的信息，可能产生通用解决方案，在任务完成前后均给用户带来技术负担。本论文旨在通过提升任务规范抽象层级，推动实现真实场景中机器人自主性的增强。我们通过解决多个相关子问题来实现这一目标：首先，在受限移动操作场景中，开发了子任务间最优过渡点的自动发现方法，消除了人工指定这些过渡点的需求；其次，提出利用示范数据替代手动定义约束，自动描述机器人运动约束的方法；随后，在环境探索领域提出灵活的任务规范框架，仅需用户提供一组感兴趣的分位数，即可让机器人直接推荐待研究的环境位置；接着，系统研究了引入机器人团队对任务规范的影响，证明多机器人团队在特定规范条件下（包括启用机器人间通信）具有提升性能的能力；最后，提出了通信协议方法，使机器人能自主选择有用但有限的信息与其他机器人共享。