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In this interview, we take a look at imitation learning in robotics through the eyes of Michael Laskey, a PhD student at UC Berkley. Laskey explains how the technology is being applied to advance robotic manipulation to create better and safer machines.

Manipulation is one of the hardest things for robots to do. One of the biggest challenges is obtaining demonstrations suitable for learning. One of the ways deep learning can be applied to robotics is through imitation learning where a human is showing the robot what to do. The concept of imitation learning is to provide the robot with prior information about its atmosphere by mirroring human actions. For example, the robot learns an action and mimics the behavior, such as folding bed sheets.

Laskey does also address some of the shortcomings of imitation learning, including the need for some supervision as well as data. Additionally, when robots make mistakes, they can’t recover and errors can multiple. This is a must-listen as we do a deep-dive into the fascinating world of imitation learning in the field of robotics.

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