Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration

10 Jul 2017Rouhollah RahmatizadehPooya AbolghasemiLadislau BölöniSergey Levine

We propose a technique for multi-task learning from demonstration that trains the controller of a low-cost robotic arm to accomplish several complex picking and placing tasks, as well as non-prehensile manipulation. The controller is a recurrent neural network using raw images as input and generating robot arm trajectories, with the parameters shared across the tasks... (read more)

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