Long-Horizon Visual Imitation Learning via Plan and Code Reflection
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908719464316928 |
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| author | Chen, Quan Shi, Chenrui Chen, Qi Wu, Yuwei Gao, Zhi Zhang, Xintong Gao, Rui Wu, Kun Jia, Yunde |
| author_facet | Chen, Quan Shi, Chenrui Chen, Qi Wu, Yuwei Gao, Zhi Zhang, Xintong Gao, Rui Wu, Kun Jia, Yunde |
| contents | Learning from long-horizon demonstrations with complex action sequences presents significant challenges for visual imitation learning, particularly in understanding temporal relationships of actions and spatial relationships between objects. In this paper, we propose a new agent framework that incorporates two dedicated reflection modules to enhance both plan and code generation. The plan generation module produces an initial action sequence, which is then verified by the plan reflection module to ensure temporal coherence and spatial alignment with the demonstration video. The code generation module translates the plan into executable code, while the code reflection module verifies and refines the generated code to ensure correctness and consistency with the generated plan. These two reflection modules jointly enable the agent to detect and correct errors in both the plan generation and code generation, improving performance in tasks with intricate temporal and spatial dependencies. To support systematic evaluation, we introduce LongVILBench, a benchmark comprising 300 human demonstrations with action sequences of up to 18 steps. LongVILBench emphasizes temporal and spatial complexity across multiple task types. Experimental results demonstrate that existing methods perform poorly on this benchmark, whereas our new framework establishes a strong baseline for long-horizon visual imitation learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05368 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Long-Horizon Visual Imitation Learning via Plan and Code Reflection Chen, Quan Shi, Chenrui Chen, Qi Wu, Yuwei Gao, Zhi Zhang, Xintong Gao, Rui Wu, Kun Jia, Yunde Robotics Artificial Intelligence Machine Learning I.2.9; I.2.10 Learning from long-horizon demonstrations with complex action sequences presents significant challenges for visual imitation learning, particularly in understanding temporal relationships of actions and spatial relationships between objects. In this paper, we propose a new agent framework that incorporates two dedicated reflection modules to enhance both plan and code generation. The plan generation module produces an initial action sequence, which is then verified by the plan reflection module to ensure temporal coherence and spatial alignment with the demonstration video. The code generation module translates the plan into executable code, while the code reflection module verifies and refines the generated code to ensure correctness and consistency with the generated plan. These two reflection modules jointly enable the agent to detect and correct errors in both the plan generation and code generation, improving performance in tasks with intricate temporal and spatial dependencies. To support systematic evaluation, we introduce LongVILBench, a benchmark comprising 300 human demonstrations with action sequences of up to 18 steps. LongVILBench emphasizes temporal and spatial complexity across multiple task types. Experimental results demonstrate that existing methods perform poorly on this benchmark, whereas our new framework establishes a strong baseline for long-horizon visual imitation learning. |
| title | Long-Horizon Visual Imitation Learning via Plan and Code Reflection |
| topic | Robotics Artificial Intelligence Machine Learning I.2.9; I.2.10 |
| url | https://arxiv.org/abs/2509.05368 |