Robots That Ask for Help
Free while signed in. Answers cite the passages they came from.
A framework for calibrating LLM-based robot planners so they ask for help when uncertain.
Uncertainty alignment: Measures and aligns the uncertainty of LLM planners so help-requests correlate with real task difficulty.
Conformal prediction: Uses conformal prediction to provide rigorous statistical guarantees on when to defer to humans.
Safer autonomy: Reduces the risk of silent failures in robot deployments where an LLM confidently executes wrong plans.
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.
Get next week’s papers.
The same picks and the same summaries, in your inbox. Free, and 176 issues deep.
Subscribe on Substack