Asking Smarter Questions: Targeted Active Preference Learning for Underspecified Rewards
Reward learning typically assumes the human's input covers everything that matters for the task. In practice, humans can only attend to so much at once, so their teaching naturally captures some features---or aspects of the task---better than others. Prior work has shown how the robot can identify which features were poorly supervised directly from its teaching data. But the current solution is to ask the human to try again, putting the burden on someone who already struggled to convey those features. We propose shifting this burden to the robot. Our key insight is that the same information that tells the robot what it doesn't know can help it ask better questions. Preference queries offe...
| Authors | Helena Merker, Nick Walker, Andreea Bobu |
|---|---|
| Primary category | cs.RO |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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