GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks
Summary
Introduces Graph-as-Policy (GaP), a multi-agent coding harness that generates directed computation graphs from a modular robot skill library and uses parallel simulation to iteratively refine task execution, achieving significantly higher success rates on variable automation tasks compared to baselines.
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Paper page - GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks
Source: https://huggingface.co/papers/2607.05369 Authors:
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Abstract
Graph-as-Policy system combines modular robot skills with multi-agent coding to improve reliability in variable automation tasks through parallel simulation refinement.
For robots to work reliably in commercial and industrial applications, can recent advances inagentic coding systemscombine interpretable robot programming with the open-world adaptability ofmodel-free policies? We focus on “Variational Automation” (VA), a class of tasks that have larger variations in object geometry and pose than fixed automation.Model-free policiesoften struggle to close the reliability gap for VA tasks, which must be executed persistently and reliably in commercial and industrial applications. Motivated by prior work onTask and Motion Planning(TAMP) and theRobot Operating System(ROS), we introduce Graph-as-Policy (GaP), a multi-agent coding harness that generatesdirected computation graphswith perception, planning, and control nodes from aModular Open Robot Skill Library(MORSL). GaP then generates aninternal simulation environmentto rehearse task instances with different graphs in parallel to iteratively refine the graph structure and parameters to improve success rates and throughput. Evaluation with 8 new open VA task benchmarks, 4 in-simulation and 4 in real-world, suggests that GaP can achieve success rates that significantly outperform baselines. Details, code, and data can be found online: https://graph-robots.github.io/gap
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