RoboInspector: Unveiling the Unreliability of Policy Code for LLM-enabled Robotic Manipulation
A new preprint introduces RoboInspector, a pipeline designed to systematically identify and characterize unreliable behaviors in policy code generated by large language models (LLMs) for robotic manipulation. The study analyzes 216 combinations of tasks, instructions, and LLMs, uncovering four main types of unreliable behaviors that can lead to manipulation failures. The authors also propose a refinement approach, guided by feedback from failed policy code, which improves reliability by up to 35% in both simulated and real-world robotic environments.
Why it matters: This work exposes critical reliability challenges in LLM-driven robotic manipulation and demonstrates a practical method to significantly reduce failures, which is important for real-world deployment.
Full story at: arXiv Robotics ↗