As robots become more commonplace, the tools to facilitate knowledge transfer from human to robot will be vital, especially for non-technical users. While some ongoing work considers the role of human reinforcement in intelligent algorithms, the burden of learning is often placed solely on the computer. These approaches neglect the expressive capabilities of humans, especially regarding our ability to quickly refine motor skills. Thus, when designing autonomous robots that interact with humans, not only is it important to leverage machine learning, but it is also very useful to have the tools in place to facilitate the transfer of knowledge between man and machine. We introduce such a tool for enabling a human to transfer motion learning capabilities to a robot. In this paper, we propose a general framework for Motion Acquisition for Robots through Iterative Online Evaluative Training (MARIOnET). Specifically, MARIOnET represents a direct and real-time interface between a human in a motion-capture suit and a robot, with a training process that provides a convenient human interface and requires no technical knowledge. Our approach exploits the ability at which humans are able to learn and refine fine-motor skills. Implemented on two robots (one quadruped and one biped), our results indicate that both technical and non-technical users are able to harness MARIOnET to quickly improve a robot's performance of a task requiring fine-motor skills.

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In International Conference on Autonomous Agents and Multiagent Systems, 2010.

@inproceedings{setapen:aamas10, title={MARIOnET: Motion Acquisition for Robots through Iterative Online Evaluative Training}, author={Adam Setapen and Michael Quinlan and Peter Stone}, booktitle={International Conference on Autonomous Agents and Multiagent Systems}, url="http://nn.cs.utexas.edu/?setapen:aamas10", year={2010} }