Affordance-Graphed Task Worlds: Self-Evolving Task Generation for Scalable Embodied Learning
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917270793486336 |
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| author | Liu, Xiang Cui, Sen Yao, Guocai Cao, Zhong Ma, Jingheng Zhang, Min Zhang, Changshui |
| author_facet | Liu, Xiang Cui, Sen Yao, Guocai Cao, Zhong Ma, Jingheng Zhang, Min Zhang, Changshui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_12065 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Affordance-Graphed Task Worlds: Self-Evolving Task Generation for Scalable Embodied Learning Liu, Xiang Cui, Sen Yao, Guocai Cao, Zhong Ma, Jingheng Zhang, Min Zhang, Changshui Robotics 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. |
| title | Affordance-Graphed Task Worlds: Self-Evolving Task Generation for Scalable Embodied Learning |
| topic | Robotics |
| url | https://arxiv.org/abs/2602.12065 |