Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials
Teaching an assistive robotic manipulator to move objects in a cluttered table requires demonstrations from expert operators, but what if the experts are individuals with motor disabilities? Batzianoulis et al. propose a learning approach which combines robot autonomy and a brain-computer interfacin...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-12-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-021-02891-8 |
| Summary: | Teaching an assistive robotic manipulator to move objects in a cluttered table requires demonstrations from expert operators, but what if the experts are individuals with motor disabilities? Batzianoulis et al. propose a learning approach which combines robot autonomy and a brain-computer interfacing that decodes whether the generated trajectories meet the user’s criteria, and show how their system enables the robot to learn individual user’s preferred behaviors using less than five demonstrations that are not necessarily optimal. |
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| ISSN: | 2399-3642 |