通过空间图解指令教机器人去哪里以及如何行动：基于人类草图
Teaching Robots Where To Go And How To Act With Human Sketches via Spatial Diagrammatic Instructions
AI总结：
本文提出空间图解指令方法，让操作员通过草图指定空间目标与约束，并学习空间指令图以优化移动机械臂基座放置，实现更高质量和更快速的求解。
摘要：
本文介绍了空间图解指令（Spatial Diagrammatic Instructions, SDIs），这是一种供人类操作员指定与工作环境中空间区域相关的目标和约束的方法。人类操作员能够直接在相机图像上勾画出与目标和约束相对应的区域。这些草图被投影到三维空间坐标上，并在其上学习连续的空间指令图（Spatial Instruction Maps, SIMs）。这些图随后可以被集成到机器人任务的优化问题中。特别是，我们展示了如何应用空间图解指令来解决移动机械臂的基座放置问题（Base Placement Problem），该问题涉及将机械臂放置在何处以最好地促进特定任务。人类操作员可以通过草图指定操作任务感兴趣的空间区域以及移动机械臂允许所处的区域。然后，求解一个优化问题，该问题在机械臂保持在允许区域内的同时，最大化机械臂在指定感兴趣区域上的可达性或覆盖范围。我们提供了广泛的实证评估，并表明我们的空间指令图公式能够准确表示用户指定的图解指令。此外，我们证明了我们的图解式方法在移动基座放置问题上能够获得更高质量的解决方案和更快的运行时间。
英文摘要：
This paper introduces Spatial Diagrammatic Instructions (SDIs), an approach for human operators to specify objectives and constraints that are related to spatial regions in the working environment. Human operators are enabled to sketch out regions directly on camera images that correspond to the objectives and constraints. These sketches are projected to 3D spatial coordinates, and continuous Spatial Instruction Maps (SIMs) are learned upon them. These maps can then be integrated into optimization problems for tasks of robots. In particular, we demonstrate how Spatial Diagrammatic Instructions can be applied to solve the Base Placement Problem of mobile manipulators, which concerns the best place to put the manipulator to facilitate a certain task. Human operators can specify, via sketch, spatial regions of interest for a manipulation task and permissible regions for the mobile manipulator to be at. Then, an optimization problem that maximizes the manipulator's reachability, or coverage, over the designated regions of interest while remaining in the permissible regions is solved. We provide extensive empirical evaluations, and show that our formulation of Spatial Instruction Maps provides accurate representations of user-specified diagrammatic instructions. Furthermore, we demonstrate that our diagrammatic approach to the Mobile Base Placement Problem enables higher quality solutions and faster runtime.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
