GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks
For robots to work reliably in commercial and industrial applications, can recent advances in agentic coding systems combine interpretable robot programming with the open-world adaptability of model-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 policies often 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 on Task and Motion Planning (TAMP) and the Robot Oper
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- PossiblePossibly related (embedding) · 54%Agentic AI for Robot Teams →
- PossiblePossibly related (embedding) · 53%Learning Structured Reasoning via Tractable Trajectory Control - Apple Machine Learning Research →
- PossiblePossibly related (embedding) · 51%Unity-Technologies/ml-agents →
- PossiblePossibly related (embedding) · 50%airbus/scikit-decide →
- FuzzySimilar title/name (fuzzy) · 59%AgentCore-8B →
“Fuzzy title match (0.73): “GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For” ≈ “AgentCore-8B””
- LinkedLinked via arxiv author · 85%Kaiyuan Chen →
“GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks”
- LinkedLinked via arxiv author · 85%Shuangyu Xie →
“GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks”
- LinkedLinked via arxiv author · 85%Letian Fu →
“GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks”