| Abstract | This paper contributes a novel framework that enables a robotic agent to efficiently learn and synthesize believable handwriting motion. We situate the framework as a foundation with the goal of allowing children to observe, correct and engage with the robot to learn themselves the handwriting skill. The framework adapts the principle behind ensemble methods - where improved performance is obtained by combining the output of multiple simple algorithms - in an inverse optimal control problem. This integration addresses the challenges of rapid extraction and representation of multiple-mode motion trajectories, with the cost forms which are transferable and interpretable in the development of the robot compliance control. It also introduces the incorporation of a human movement inspired feature, which provides intuitive motion modulation to generalize the synthesis with poor robotic written samples for children to identify and correct. We present the results on the success of synthesizing a variety of natural-looking motion samples based upon the learned cost functions. The framework is validated by a user study, where the synthesized dynamical motion is shown to be hard to distinguish from the real human handwriting. | |
| Year | 2016 | |
| Keywords | Reinforcement Learning;Social Robotic Companions; | |
| Authors | Hang Yin, Patrícia Alves-Oliveira, Francisco S. Melo, Aude Billard, Ana Paiva | |
| Booktitle | Proceedings of International Joint Conference on Artificial Intelligence (IJCAI) | |
| Month | July 09-15 | |
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@inproceedings { yin16, abstract = {This paper contributes a novel framework that enables a robotic agent to efficiently learn and synthesize believable handwriting motion. We situate the framework as a foundation with the goal of allowing children to observe, correct and engage with the robot to learn themselves the handwriting skill. The framework adapts the principle behind ensemble methods - where improved performance is obtained by combining the output of multiple simple algorithms - in an inverse optimal control problem. This integration addresses the challenges of rapid extraction and representation of multiple-mode motion trajectories, with the cost forms which are transferable and interpretable in the development of the robot compliance control. It also introduces the incorporation of a human movement inspired feature, which provides intuitive motion modulation to generalize the synthesis with poor robotic written samples for children to identify and correct. We present the results on the success of synthesizing a variety of natural-looking motion samples based upon the learned cost functions. The framework is validated by a user study, where the synthesized dynamical motion is shown to be hard to distinguish from the real human handwriting. }, booktitle = {Proceedings of International Joint Conference on Artificial Intelligence (IJCAI)}, howpublished = {In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI)}, journal = {Proceedings of International Joint Conference on Artificial Intelligence (IJCAI)}, keywords = {Reinforcement Learning;Social Robotic Companions;}, month = {July 09-15}, title = {Synthesizing Robotic Handwriting Motion by Learning from Human Demonstrations}, year = {2016}, author = {Hang Yin and Patrícia Alves-Oliveira and Francisco S. Melo and Aude Billard and Ana Paiva} } |
Synthesizing Robotic Handwriting Motion by Learning from Human Demonstrations
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