A Robust Method to Predict Temporal Aspects of Actions by Observation
Emmanouil Hourdakis, Panos Trahanias
Abstract
The ability to predict the duration of an activity can enable a robot to plan its behaviors ahead, interact seamlessly with other humans, by coordinating its actions, and allocate effort and resources to tasks that are time-constrained or critical. Despite its usefulness, models that examine the temporal properties of an activity remain relatively unexplored. In the current paper we present, to the best of our knowledge, the first method that can estimate temporal properties of an activity by observation. We evaluate it on three use-cases (i) wiping a table, (ii) chopping vegetables and (iii) cleaning the floor, using ground truth data from real demonstrations, and show that it can make predictions with high accuracy and little training. In addition, we investigate different methods to approximate the progress of each task, and demonstrate how a model can generalize, by reusing part of it in different activities.
BibTeX
@inproceedings{icra2018_arobustmethodtop,
title = {A Robust Method to Predict Temporal Aspects of Actions by Observation},
author = {Emmanouil Hourdakis and Panos Trahanias},
booktitle = {ICRA 2018},
year = {2018}
}