通过草图指导机器人：基于概率图表演示的示范学习
Instructing Robots by Sketching: Learning from Demonstration via Probabilistic Diagrammatic Teaching
AI总结：
本文提出图表演示范式及RPTL框架，通过用户在2D图像上勾画轨迹，利用光线追踪和概率建模生成3D运动轨迹，使机器人无需动觉示教或遥操作即可学习新技能，并在仿真与真实机器人上验证了有效性。
摘要：
示范学习（Learning from Demonstration, LfD）使机器人能够通过模仿专家示范来获取新技能，让用户以直观的方式传达指令。近年来LfD的进展通常依赖动觉示教或遥操作作为用户指定示范的媒介。动觉示教需要对机器人进行物理操作，而遥操作则要求熟练使用额外硬件。本文引入了一种称为图表演示（Diagrammatic Teaching）的LfD替代范式。图表演示旨在通过提示用户在场景的2D图像上勾画示范轨迹来教机器人学习新技能，随后将这些轨迹合成为3D任务空间中运动轨迹的生成模型。此外，我们提出了用于图表演示的光线追踪概率轨迹学习（Ray-tracing Probabilistic Trajectory Learning, RPTL）框架。RPTL从2D草图中提取时变概率密度，应用光线追踪在3D笛卡尔空间中找到对应区域，并将运动轨迹的概率模型拟合到这些区域。随后可以从概率模型中生成模仿用户所勾画轨迹的新运动轨迹。我们在仿真和真实机器人上对框架进行了实证验证，包括一个固定基座机械臂和一个四足机器人搭载的机械臂。
英文摘要：
Learning for Demonstration (LfD) enables robots to acquire new skills by imitating expert demonstrations, allowing users to communicate their instructions in an intuitive manner. Recent progress in LfD often relies on kinesthetic teaching or teleoperation as the medium for users to specify the demonstrations. Kinesthetic teaching requires physical handling of the robot, while teleoperation demands proficiency with additional hardware. This paper introduces an alternative paradigm for LfD called Diagrammatic Teaching. Diagrammatic Teaching aims to teach robots novel skills by prompting the user to sketch out demonstration trajectories on 2D images of the scene, these are then synthesised as a generative model of motion trajectories in 3D task space. Additionally, we present the Ray-tracing Probabilistic Trajectory Learning (RPTL) framework for Diagrammatic Teaching. RPTL extracts time-varying probability densities from the 2D sketches, applies ray-tracing to find corresponding regions in 3D Cartesian space, and fits a probabilistic model of motion trajectories to these regions. New motion trajectories, which mimic those sketched by the user, can then be generated from the probabilistic model. We empirically validate our framework both in simulation and on real robots, which include a fixed-base manipulator and a quadruped-mounted manipulator.
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。
