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The paper introduces Affordance-Graphed Task Worlds (AGT-World), a framework that automatically generates interactive simulated environments and robot task policies from real-world observations by formalizing the task space as a structured graph. This graph-based approach allows for hierarchical decomposition of complex goals into atomic primitives, addressing the limitations of random proposal or static replication methods. The authors further incorporate a self-evolution mechanism with hybrid feedback, combining Vision-Language Model reasoning and geometric verification, to refine policies.
Forget random exploration: this framework uses affordance graphs and self-evolution to create coherent, complex robot tasks in simulation, leading to better policy learning and generalization.
Training robotic policies directly in the real world is expensive and unscalable. Although generative simulation enables large-scale data synthesis, current approaches often fail to generate logically coherent long-horizon tasks and struggle with dynamic physical uncertainties due to open-loop execution. To address these challenges, we propose Affordance-Graphed Task Worlds (AGT-World), a unified framework that autonomously constructs interactive simulated environments and corresponding robot task policies based on real-world observations. Unlike methods relying on random proposals or static replication, AGT-World formalizes the task space as a structured graph, enabling the precise, hierarchical decomposition of complex goals into theoretically grounded atomic primitives. Furthermore, we introduce a Self-Evolution mechanism with hybrid feedback to autonomously refine policies, combining Vision-Language Model reasoning and geometric verification. Extensive experiments demonstrate that our method significantly outperforms in success rates and generalization, achieving a self-improving cycle of proposal, execution, and correction for scalable robot learning.
