Cracking Mobile Manipulation: From Failure Diagnosis to Agentic Robot System
Overview
This research addresses the limitations of VLA models in mobile manipulation by diagnosing failure regimes and developing an agentic intervention system, improving success rates on two benchmarks. The authors plan to release their code for open-source research.
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“13/20 on real-robot tasks. These results suggest that reliable mobile manipulation can be advanced by diagnosing and intervening on the current bottleneck, rather than uniformly scaling the underlying policy. Our code will be released at https://github.com/PKU-VaLuE-Lab/Cracking-Mobile-Manipulation.git.”
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- arXiv Robotics — research abstractsCracking Mobile Manipulation: From Failure Diagnosis to Agentic Robot System
This research investigates the limitations of vision-language-action (VLA) models in long-horizon mobile manipulation. By diagnosing failure regimes in task planning, navigation, and local manipulation, the authors develop an agentic intervention system that improves success rates from 19% to 49% on RoboCasa365, and from 14% to 37% on BEHAVIOR-1K. The system incorporates demonstration-based planning, adaptive memory, and base adjustment, demonstrating the value of targeted interventions over uniform policy scaling.
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