用低成本传感器夹爪记录人操作，让机器人快速学会组装任务。
Sensorized gripper for human demonstrations
- 用现成零件组装传感器夹爪，采集人类示范动作
- 仅需几次短时演示，机器人即可成功复现装配任务
- 结合高斯混合模型与阻抗控制，适合非结构化环境学习
编程简易性是使机器人在非结构化环境中普及的关键。本文提出一种由市售部件构建的传感器化夹爪，用于记录人类完成“盒中盒”装配任务的示范。仅通过少数几次、间隔较短的演示，机器人便能成功重复该任务。采用笛卡尔运动规划方法，在求解关节空间解的同时优化机器人位置，以最大化操作性能。利用高斯混合模型（GMM）提取人类示范的统计特征，并通过阻抗控制指令机器人执行。
原文摘要 · Abstract (English)
Ease of programming is a key factor in making robots ubiquitous in unstructured environments. In this work, we present a sensorized gripper built with off-the-shelf parts, used to record human demonstrations of a box in box assembly task. With very few trials of short interval timings each, we show that a robot can repeat the task successfully. We adopt a Cartesian approach to robot motion generation by computing the joint space solution while concurrently solving for the optimal robot position, to maximise manipulability. The statistics of the human demonstration are extracted using Gaussian Mixture Models (GMM) and the robot is commanded using impedance control.
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