Building Explicit World Model for Zero-Shot Open-World Object Manipulation
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917341608017920 |
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| author | Li, Xiaotong Chen, Gang Alonso-Mora, Javier |
| author_facet | Li, Xiaotong Chen, Gang Alonso-Mora, Javier |
| contents | Open-world object manipulation remains a fundamental challenge in robotics. While Vision-Language-Action (VLA) models have demonstrated promising results, they rely heavily on large-scale robot action demonstrations, which are costly to collect and can hinder out-of-distribution generalization. In this paper, we propose an explicit-world-model-based framework for open-world manipulation that achieves zero-shot generalization by constructing a physically grounded digital twin of the environment. The framework integrates open-set perception, digital-twin reconstruction, sampling and evaluation of interaction strategies. By constructing a digital twin of the environment, our approach efficiently explores and evaluates manipulation strategies in physic-enabled simulator and reliably deploys the chosen strategy to the real world. Experimentally, the proposed framework is able to perform multiple open-set manipulation tasks without any task-specific action demonstrations, proving strong zero-shot generalization on both the task and object levels. Project Page: https://bojack-bj.github.io/projects/thesis/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13825 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Building Explicit World Model for Zero-Shot Open-World Object Manipulation Li, Xiaotong Chen, Gang Alonso-Mora, Javier Robotics Open-world object manipulation remains a fundamental challenge in robotics. While Vision-Language-Action (VLA) models have demonstrated promising results, they rely heavily on large-scale robot action demonstrations, which are costly to collect and can hinder out-of-distribution generalization. In this paper, we propose an explicit-world-model-based framework for open-world manipulation that achieves zero-shot generalization by constructing a physically grounded digital twin of the environment. The framework integrates open-set perception, digital-twin reconstruction, sampling and evaluation of interaction strategies. By constructing a digital twin of the environment, our approach efficiently explores and evaluates manipulation strategies in physic-enabled simulator and reliably deploys the chosen strategy to the real world. Experimentally, the proposed framework is able to perform multiple open-set manipulation tasks without any task-specific action demonstrations, proving strong zero-shot generalization on both the task and object levels. Project Page: https://bojack-bj.github.io/projects/thesis/ |
| title | Building Explicit World Model for Zero-Shot Open-World Object Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.13825 |