Imitation learning on a robotic arm and a wheeled robot (VIDEO)
Caption
Princeton researchers used imitation learning to improve the success of machine learning-based robot control policies. Simulation experiments included (1) a robotic arm tasked with grasping and lifting drinking mugs of various sizes, shapes and materials; (2) the arm pushing a box across a table; and (3) a wheeled robot navigating around furniture in a home-like environment. The researchers deployed the policies learned from the mug-grasping and box-pushing tasks on a robotic arm in the laboratory, which was able to pick up 25 different mugs by grasping their rims between its two finger-like grippers.
Credit
Intelligent Robot Motion Lab at Princeton University
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