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Sensorized Gripper for Human Demonstrations
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AI
DOI：10.1007/978-981-96-7703-0_36.png)
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Abstract
En 中文
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. The success/failure of the box in box assembly task is presented, along with the haptic mismatch between the demonstration and robot replay. We conclude by identifying trajectory adaptation in the event of failure as a future scope.
Keywords:
Learning from demonstration
Impedance control
Sensorized tools
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