LLM planners for robots often produce dangerous plans even when planning succeeds, with safety awareness staying flat as model scale improves planning ability.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2representative citing papers
HUME lets robots generate, plan over, and actively verify object-centric hypotheses from foundation models so incomplete symbolic models become usable for open-world household tasks.
citing papers explorer
-
Using large language models for embodied planning introduces systematic safety risks
LLM planners for robots often produce dangerous plans even when planning succeeds, with safety awareness staying flat as model scale improves planning ability.
-
Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning
HUME lets robots generate, plan over, and actively verify object-centric hypotheses from foundation models so incomplete symbolic models become usable for open-world household tasks.