We introduce GeoSACS, a geometric framework for shared autonomy (SA). In variable environments, SA methods can be used to combine robotic capabilities with real-time human input in a way that offloads the physical task from the human. To remain intuitive, it can be helpful to simplify requirements for human input (i.e., reduce the dimensionality), which create challenges for to map low-dimensional human inputs to the higher dimensional control space of robots without requiring large amounts of data. We built GeoSACS on canal surfaces, a geometric framework that represents potential robot trajectories as a canal from as few as two demonstrations. GeoSACS maps user corrections on the cross-sections of this canal to provide an efficient SA framework. We extend canal surfaces to consider orientation and update the control frames to support intuitive mapping from user input to robot motions. Finally, we demonstrate GeoSACS in two preliminary studies, including a complex manipulation task where a robot loads laundry into a washer.

翻译：我们提出GeoSACS，一种用于共享自主（SA）的几何框架。在可变环境中，SA方法可将机器人能力与实时人类输入相结合，以减轻人类的体力任务负担。为保持直观性，简化人类输入需求（即降低维度）可能有所帮助，但这会带来挑战：如何在无需大量数据的前提下，将低维人类输入映射到机器人更高维的控制空间。我们基于管道曲面构建GeoSACS——这一几何框架通过仅需两次演示即可将潜在机器人轨迹表示为管道。GeoSACS将用户在管道截面上的修正动作映射为高效的SA框架。我们扩展管道曲面以考虑方向因素，并更新控制框架以支持从用户输入到机器人运动的直观映射。最后，通过两项初步研究（包括机器人将衣物装入洗衣机的复杂操作任务）验证了GeoSACS的有效性。