Affordance-Graphed Task Worlds: Self-Evolving Task Generation for Scalable Embodied Learning

• URL: http://arxiv.org/abs/2602.12065v1
• Date: Thu, 12 Feb 2026 15:23:45 GMT
• Title: Affordance-Graphed Task Worlds: Self-Evolving Task Generation for Scalable Embodied Learning
• Abstract summary: Affordance-Graphed Task Worlds (AGT-World) is a framework that autonomously constructs interactive simulated environments and corresponding robot task policies.<n>Unlike methods relying on random proposals or static replication, AGT-World formalizes the task space as a structured graph.<n>Our method significantly outperforms in success rates and generalization, achieving a self-improving cycle of proposal, execution, and correction for scalable robot learning.
• Score: 38.10198732103412
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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