Robots That Ask for Help
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Paper summary
A framework for calibrating LLM-based robot planners so they ask for help when uncertain.
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01
Uncertainty alignment: Measures and aligns the uncertainty of LLM planners so help-requests correlate with real task difficulty.
02
Conformal prediction: Uses conformal prediction to provide rigorous statistical guarantees on when to defer to humans.
03
Safer autonomy: Reduces the risk of silent failures in robot deployments where an LLM confidently executes wrong plans.
04
Human-robot collaboration: An early contribution to the know-when-you-don't-know literature for LLM-driven agents - a theme that became central to 2024 agent safety work.