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Naomi Fitter on Muck Rack

Naomi Fitter

As seen in: Frontiers, IEEE Spectrum
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Articles

Adaptive querying for reward learning from human feedback

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By Yashwanthi Anand, Kevin Sabbe, Naomi Fitter, Sandhya Saisubramanian, Nnamdi Nwagwu
| Frontiers
Oregon State University, Corvallis, OR, United States Learning from human feedback is a popular approach to train robots to adapt to user preferences and improve safety. Existing approaches typically consider a single querying (interaction) format when seeking human feedback and do not leverage multiple modes of user interaction with a robot.
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Social Robots That Curse: Why and How to Study Them Original

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By Naomi Fitter
| IEEE Spectrum Verified
The robots that share our public spaces today are so demure. Social robots and service robots aim to avoid offense, erring toward polite airs, positive emotions, and obedience. In some ways, this makes sense—would you really want to have a yelling match with a delivery robot in a hotel? Probably not, even if you’re in New York City and trying to absorb the local culture. In other ways, this passive social robot design aligns with paternalistic standards that link assistance to subservience.
Open in Who Shared View Duplicates (3) Issue with article?

Cursing Robots: Challenging Norms With Humor

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By Naomi Fitter
| IEEE Spectrum Verified
The robots that share our public spaces today are so demure. Social robots and service robots aim to avoid offense, erring toward polite airs, positive emotions, and obedience. In some ways, this makes sense—would you really want to have a yelling match with a delivery robot in a hotel? Probably not, even if you’re in New York City and trying to absorb the local culture. In other ways, this passive social robot design aligns with paternalistic standards that link assistance to subservience.
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