DeCo: Task Decomposition and Skill Composition for Zero-Shot Generalization in Long-Horizon 3D Manipulation
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914330176389120 |
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| author | Chen, Zixuan Yin, Junhui Chen, Yangtao Huo, Jing Tian, Pinzhuo Shi, Jieqi Hou, Yiwen Li, Yinchuan Gao, Yang |
| author_facet | Chen, Zixuan Yin, Junhui Chen, Yangtao Huo, Jing Tian, Pinzhuo Shi, Jieqi Hou, Yiwen Li, Yinchuan Gao, Yang |
| contents | Generalizing language-conditioned multi-task imitation learning (IL) models to novel long-horizon 3D manipulation tasks is challenging. To address this, we propose DeCo (Task Decomposition and Skill Composition), a model-agnostic framework that enhances zero-shot generalization to compositional long-horizon manipulation tasks. DeCo decomposes IL demonstrations into modular atomic tasks based on gripper-object interactions, creating a dataset that enables models to learn reusable skills. At inference, DeCo uses a vision-language model (VLM) to parse high-level instructions, retrieve relevant skills, and dynamically schedule their execution. A spatially-aware skill-chaining module ensures smooth, collision-free transitions between skills. We introduce DeCoBench, a benchmark designed to evaluate compositional generalization in long-horizon manipulation tasks. DeCo improves the success rate of three IL models, RVT-2, 3DDA, and ARP, by 66.67%, 21.53%, and 57.92%, respectively, on 12 novel tasks. In real-world experiments, the DeCo-enhanced model, trained on only 6 atomic tasks, completes 9 novel tasks in zero-shot, with a 53.33% improvement over the baseline model. Project website: https://deco226.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_00527 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DeCo: Task Decomposition and Skill Composition for Zero-Shot Generalization in Long-Horizon 3D Manipulation Chen, Zixuan Yin, Junhui Chen, Yangtao Huo, Jing Tian, Pinzhuo Shi, Jieqi Hou, Yiwen Li, Yinchuan Gao, Yang Robotics Generalizing language-conditioned multi-task imitation learning (IL) models to novel long-horizon 3D manipulation tasks is challenging. To address this, we propose DeCo (Task Decomposition and Skill Composition), a model-agnostic framework that enhances zero-shot generalization to compositional long-horizon manipulation tasks. DeCo decomposes IL demonstrations into modular atomic tasks based on gripper-object interactions, creating a dataset that enables models to learn reusable skills. At inference, DeCo uses a vision-language model (VLM) to parse high-level instructions, retrieve relevant skills, and dynamically schedule their execution. A spatially-aware skill-chaining module ensures smooth, collision-free transitions between skills. We introduce DeCoBench, a benchmark designed to evaluate compositional generalization in long-horizon manipulation tasks. DeCo improves the success rate of three IL models, RVT-2, 3DDA, and ARP, by 66.67%, 21.53%, and 57.92%, respectively, on 12 novel tasks. In real-world experiments, the DeCo-enhanced model, trained on only 6 atomic tasks, completes 9 novel tasks in zero-shot, with a 53.33% improvement over the baseline model. Project website: https://deco226.github.io. |
| title | DeCo: Task Decomposition and Skill Composition for Zero-Shot Generalization in Long-Horizon 3D Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.00527 |