Vision-Based Multi-Task Manipulation For Inexpensive Robots Using End-To-End Learning From Demonstration

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.

Publication Date

9-10-2018

Publication Title

Proceedings - IEEE International Conference on Robotics and Automation

Number of Pages

3758-3765

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICRA.2018.8461076

Socpus ID

85063141966 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85063141966

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