Diffusion Trajectory-guided Policy for Long-horizon Robot Manipulation
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915811494461440 |
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| author | Fan, Shichao Yang, Quantao Liu, Yajie Wu, Kun Che, Zhengping Liu, Qingjie Wan, Min |
| author_facet | Fan, Shichao Yang, Quantao Liu, Yajie Wu, Kun Che, Zhengping Liu, Qingjie Wan, Min |
| contents | Recently, Vision-Language-Action models (VLA) have advanced robot imitation learning, but high data collection costs and limited demonstrations hinder generalization and current imitation learning methods struggle in out-of-distribution scenarios, especially for long-horizon tasks. A key challenge is how to mitigate compounding errors in imitation learning, which lead to cascading failures over extended trajectories. To address these challenges, we propose the Diffusion Trajectory-guided Policy (DTP) framework, which generates 2D trajectories through a diffusion model to guide policy learning for long-horizon tasks. By leveraging task-relevant trajectories, DTP provides trajectory-level guidance to reduce error accumulation. Our two-stage approach first trains a generative vision-language model to create diffusion-based trajectories, then refines the imitation policy using them. Experiments on the CALVIN benchmark show that DTP outperforms state-of-the-art baselines by 25% in success rate, starting from scratch without external pretraining. Moreover, DTP significantly improves real-world robot performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10040 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Diffusion Trajectory-guided Policy for Long-horizon Robot Manipulation Fan, Shichao Yang, Quantao Liu, Yajie Wu, Kun Che, Zhengping Liu, Qingjie Wan, Min Robotics Recently, Vision-Language-Action models (VLA) have advanced robot imitation learning, but high data collection costs and limited demonstrations hinder generalization and current imitation learning methods struggle in out-of-distribution scenarios, especially for long-horizon tasks. A key challenge is how to mitigate compounding errors in imitation learning, which lead to cascading failures over extended trajectories. To address these challenges, we propose the Diffusion Trajectory-guided Policy (DTP) framework, which generates 2D trajectories through a diffusion model to guide policy learning for long-horizon tasks. By leveraging task-relevant trajectories, DTP provides trajectory-level guidance to reduce error accumulation. Our two-stage approach first trains a generative vision-language model to create diffusion-based trajectories, then refines the imitation policy using them. Experiments on the CALVIN benchmark show that DTP outperforms state-of-the-art baselines by 25% in success rate, starting from scratch without external pretraining. Moreover, DTP significantly improves real-world robot performance. |
| title | Diffusion Trajectory-guided Policy for Long-horizon Robot Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2502.10040 |