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Paper WeA05.1

Lee, Kyuhwa (Imperial College of Science, Technology and Medicine), Kim, Tae-Kyun (Imperial College London), Demiris, Yiannis (Imperial College London)

Learning Reusable Task Components Using Hierarchical Activity Grammars with Uncertainties

Scheduled for presentation during the Invited Session "Embodied Inteligence - iCUB" (WeA05), Wednesday, May 16, 2012, 08:30−08:45, Meeting Room 5 (Ska)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on October 24, 2017

Keywords Learning and Adaptive Systems, Visual Learning, Computer Vision for Robotics and Automation

Abstract

We present a novel learning method using activity grammars capable of learning reusable task components from a reasonably small number of samples under noisy conditions. Our linguistic approach aims to extract the hierarchical structure of activities which can be recursively applied to help recognize unforeseen, more complicated tasks that share the same underlying structures.

To achieve this goal, our method 1) actively searches for frequently occurring action symbols that are subset of input samples to effectively discover the hierarchy, and 2) explicitly takes into account the uncertainty values associated with input symbols due to the noise inherent in low-level detectors.

In addition to experimenting with a synthetic dataset to systematically analyze the algorithm's performance, we apply our method in human-led imitation learning environment where a robot learns reusable components of the task from short demonstrations to correctly imitate more complicated, longer demonstrations of the same task category.

The results suggest that under reasonable amount of noise, our method is capable to capture the reusable structures of tasks and generalize to cope with recursions.

 

 

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