用多坐标弹性映射让机器人从示范中学习操作技能
Robot Learning Using Multi-Coordinate Elastic Maps
- 将技能编码到多种微分坐标空间，自动识别关键特征坐标
- 在仿真和真实机械臂上实现稳定书写任务，效果优于单一坐标方法
- 适合需要理解动作形状与速度的复杂操作学习场景
为学习操作技能，机器人需理解其核心特征。通过示范学习（LfD）是常见方式，但技能的关键特征可能隐藏在非笛卡尔坐标系中，如形状或速度轮廓。本文提出一种新方法，将人类示范编码至多个微分坐标空间，自动评估各坐标对技能再现的重要性。引入改进型弹性映射（Elastic Maps），支持多坐标联合建模，具备快速计算、灵活约束处理及任意数量示范输入的优势。同时提出参数自调方法以优化模型表现。在多个仿真实验和实际的UR5e机械臂书写任务中验证了该方法的有效性。
原文摘要 · Abstract (English)
To learn manipulation skills, robots need to understand the features of those skills. An easy way for robots to learn is through Learning from Demonstration (LfD), where the robot learns a skill from an expert demonstrator. While the main features of a skill might be captured in one differential coordinate (i.e., Cartesian), they could have meaning in other coordinates. For example, an important feature of a skill may be its shape or velocity profile, which are difficult to discover in Cartesian differential coordinate. In this work, we present a method which enables robots to learn skills from human demonstrations via encoding these skills into various differential coordinates, then determines the importance of each coordinate to reproduce the skill. We also introduce a modified form of Elastic Maps that includes multiple differential coordinates, combining statistical modeling of skills in these differential coordinate spaces. Elastic Maps, which are flexible and fast to compute, allow for the incorporation of several different types of constraints and the use of any number of demonstrations. Additionally, we propose methods for auto-tuning several parameters associated with the modified Elastic Map formulation. We validate our approach in several simulated experiments and a real-world writing task with a UR5e manipulator arm.
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