LLM-generated robot policy code is unreliable, with failures clustering into four behavior types that grow with task complexity and shrink with instruction detail; a failure-feedback retry improves success up to 35%.
Tongyi qianwen (qwen) - alibaba cloud, 2025
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
cs.RO 1years
2025 1verdicts
CONDITIONAL 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
RoboInspector: Unveiling the Unreliability of Policy Code for LLM-enabled Robotic Manipulation
LLM-generated robot policy code is unreliable, with failures clustering into four behavior types that grow with task complexity and shrink with instruction detail; a failure-feedback retry improves success up to 35%.