LOGIV: Graph-Verified Execution with Causal Local Repair for Long-Horizon Robot Tasks
LOGIV: Graph-Verified Execution with Causal Local Repair for Long-Horizon Robot Tasks
This paper introduces LOGIV, a framework designed to enhance the reliability of agentic robots in long-horizon tasks by combining symbolic planning with physical verification and causal repair.
Source: arXiv Robotics — research abstracts · Read original article ↗
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task completion and condition restoration than three adapted recovery baselines for every backbone on both benchmarks. On three real-robot tasks, successful trials increase from 12/30 to 17/30. These results support explicit, continuously checked task dependencies as a practical interface for reliable agentic robot execution.
Open source S5
before execution resumes. The framework operates above existing Vision-Language-Action (VLA) and World Action Model (WAM) policies without adding trainable components. Across 20 dual-arm tasks from RoboTwin V2 and BiCoord with three execution backbones, LOGIV improves mean task success without injected disturbances by 10.67 and 15.33 percentage points, respectively. Under predefined perturbations, it achieves higher
Open source S4
Source：arXiv Robotics — research abstracts · arxiv.org