TY - GEN
T1 - Towards autonomously predicting and learning a Robot's efficiency in performing tasks
AU - Dutta, Ayan
AU - Dasgupta, Prithviraj
AU - Baca, José
AU - Nelson, Carl
PY - 2013
Y1 - 2013
N2 - We consider the problem of predicting and learning the efficiency of navigation tasks being performed by a robot in an initially unknown or partially known, unstructured environment. Predicting the efficiency of a task is a crucial problem in unstructured environments as it enables the robot(s) to decide whether it is feasible to perform the task under the current environment conditions and within its available resources such as battery power. This problem is non-trivial as robot's performance changes during operation based on its operational conditions, available battery change, etc. In this paper, we have addressed this problem by using a learning-based technique that the robot uses to predict its expected efficiency for performing a new task based on the task's similarity and recentness with previously performed tasks. Experimental results show that our proposed technique can successfully predict the efficiency of a task from previous task experiences and this prediction gets better with number of tasks performed. We have also shown empirically that our model is robust to changes in environmental conditions such as localization and wheel slip noise1.
AB - We consider the problem of predicting and learning the efficiency of navigation tasks being performed by a robot in an initially unknown or partially known, unstructured environment. Predicting the efficiency of a task is a crucial problem in unstructured environments as it enables the robot(s) to decide whether it is feasible to perform the task under the current environment conditions and within its available resources such as battery power. This problem is non-trivial as robot's performance changes during operation based on its operational conditions, available battery change, etc. In this paper, we have addressed this problem by using a learning-based technique that the robot uses to predict its expected efficiency for performing a new task based on the task's similarity and recentness with previously performed tasks. Experimental results show that our proposed technique can successfully predict the efficiency of a task from previous task experiences and this prediction gets better with number of tasks performed. We have also shown empirically that our model is robust to changes in environmental conditions such as localization and wheel slip noise1.
UR - http://www.scopus.com/inward/record.url?scp=84893258907&partnerID=8YFLogxK
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U2 - 10.1109/WI-IAT.2013.157
DO - 10.1109/WI-IAT.2013.157
M3 - Conference contribution
AN - SCOPUS:84893258907
SN - 9781479929023
T3 - Proceedings - 2013 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IATW 2013
SP - 92
EP - 95
BT - Proceedings - 2013 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IATW 2013
T2 - 2013 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IATW 2013
Y2 - 17 November 2013 through 20 November 2013
ER -