R. Rahmatizadeh andP. Abolghasemi andL. Bölöni andS. Levine

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


Cite as:

R. Rahmatizadeh andP. Abolghasemi andL. Bölöni andS. Levine. Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration. In Proc. of International Conference on Robotics and Automation (ICRA-2018), pp. 3758 – 3765, May 2018.

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Abstract:

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. The controller also combines VAE-GAN-based reconstruction with autoregressive multimodal action prediction. Our results demonstrate that it is possible to learn complex manipulation tasks, such as picking up a towel, wiping an object, and depositing the towel to its previous position, entirely from raw images with direct behavior cloning. We show that weight sharing and reconstruction-based regularization substantially improve generalization and robustness, and training on multiple tasks simultaneously increases the success rate on all tasks.

BibTeX:

@inproceedings{Rahmatizadeh-2018-InexpensiveRobot,
author    = {R. Rahmatizadeh and
P. Abolghasemi and
L. B{\"o}l{\"o}ni and
S. Levine},
title     = {Vision-Based Multi-Task Manipulation for Inexpensive Robots Using
  End-To-End Learning from Demonstration},
booktitle={Proc. of International Conference on Robotics and Automation (ICRA-2018)},
location = "Brisbane, Australia",
month = "May",
year = "2018",
pages = "3758 - 3765",
x_acceptance_rate="40%",
abstract = {
  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. The controller also combines VAE-GAN-based reconstruction with autoregressive multimodal action prediction. Our results demonstrate that it is possible to learn complex manipulation tasks, such as picking up a towel, wiping an object, and depositing the towel to its previous position, entirely from raw images with direct behavior cloning. We show that weight sharing and reconstruction-based regularization substantially improve generalization and robustness, and training on multiple tasks simultaneously increases the success rate on all tasks.
 }
}

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