Title :
Robot learning by a mining tunnel inspection robot
Author :
Fenglu Ge ; Moore, William ; Antolovich, Michael ; Junbin Gao
Author_Institution :
Sch. of Comput. & Math., Charles Sturt Univ., Bathurst, NSW, Australia
Abstract :
Learning from Demonstration (LfD) is a method of teaching an agent a task by a number of suitable demonstrations. The agent will then perform the task without any further supervision. In this paper, Discrete Hidden Markov Model (DHMM) is applied to train a robot for a mining inspection task. An initial training method based on the Gaussian Mixture Model (GMM) was developed and is compared to DHMM. Results show that the learning speed based on DHMM is faster than the one for GMM and DHMM may prove to be more suitable for the mining inspection task under consideration. The proposed method has already been implemented, and some important problems on implementation are discussed.
Keywords :
Gaussian processes; control engineering computing; hidden Markov models; inspection; learning by example; mining; service robots; tunnels; DHMM; GMM; Gaussian mixture model; LfD; discrete hidden Markov model; learning from demonstration; learning speed; mining inspection task; mining tunnel inspection robot; robot learning; teaching; training method; Hidden Markov models; Inspection; Kernel; Robot kinematics; Training; Turning; Discrete Hidden Markov Model; Gaussian Mixture Model; Inspection Robot; Learning from Demonstration;
Conference_Titel :
Ubiquitous Robots and Ambient Intelligence (URAI), 2012 9th International Conference on
Conference_Location :
Daejeon
Print_ISBN :
978-1-4673-3111-1
Electronic_ISBN :
978-1-4673-3110-4
DOI :
10.1109/URAI.2012.6462974
