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Çetin Meriçli and Manuela Veloso. Biped Walk Learning On Nao Through Playback and Real-time Corrective Demonstration. In Workshop on Agents Learning Interactively from Human Teachers, 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), 2010.
We contribute a two-phase biped walk learning approach which is developed on the Aldebaran Nao humanoid robot. In the first phase, we identify and save a complete walk cycle from the motions of the robot while it is executing a given walk algorithm as a black box. We show how the robot can then play back such a recorded cycle in a loop to obtain a good open-loop walking behavior. In the second phase, we introduce an algorithm to directly modify the recorded walk cycle using real time corrective feedback provided by a human. The algorithm learns joint movement corrections to the open-loop walk based on the corrective feedback as well as the robot's sensory readings while walking autonomously. Compared to the open-loop algorithm and hand-tuned closed-loop walking algorithms, our two-phase method provides an improvement in walking stability, as demonstrated by our experimental results.
@InProceedings{MericliAAMAS2010b,
author = {Çetin Meriçli and Manuela Veloso},
title = {Biped Walk Learning On Nao Through Playback and Real-time Corrective Demonstration},
booktitle = {Workshop on Agents Learning Interactively from Human Teachers, 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010)},
abstract = {We contribute a two-phase biped walk learning approach which is developed on the Aldebaran Nao humanoid robot. In the first phase, we identify and save a complete walk cycle from the motions of the robot while it is executing a given walk algorithm as a black box. We show how the robot can then play back such a recorded cycle in a loop to obtain a good open-loop walking behavior. In the second phase, we introduce an algorithm to directly modify the recorded walk cycle using real time corrective feedback provided by a human. The algorithm learns joint movement corrections to the open-loop walk based on the corrective feedback as well as the robot's sensory readings while walking autonomously. Compared to the open-loop algorithm and hand-tuned closed-loop walking algorithms, our two-phase method provides an improvement in walking stability, as demonstrated by our experimental results.}
year = {2010},
bib2html_pubtype = {Refereed Workshop},
bib2html_rescat = {Learning from Demonstration},
bib2html_dl_pdf = {../files/cmericliAAMAS2010WalkLearningShort.pdf},
}
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