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%.
The unlocking spell on base llms: Rethinking alignment via in-context learning
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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%.