Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910926977892352 |
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| author | Hu, Yucheng Guo, Yanjiang Wang, Pengchao Chen, Xiaoyu Wang, Yen-Jen Zhang, Jianke Sreenath, Koushil Lu, Chaochao Chen, Jianyu |
| author_facet | Hu, Yucheng Guo, Yanjiang Wang, Pengchao Chen, Xiaoyu Wang, Yen-Jen Zhang, Jianke Sreenath, Koushil Lu, Chaochao Chen, Jianyu |
| contents | Visual representations play a crucial role in developing generalist robotic policies. Previous vision encoders, typically pre-trained with single-image reconstruction or two-image contrastive learning, tend to capture static information, often neglecting the dynamic aspects vital for embodied tasks. Recently, video diffusion models (VDMs) demonstrate the ability to predict future frames and showcase a strong understanding of physical world. We hypothesize that VDMs inherently produce visual representations that encompass both current static information and predicted future dynamics, thereby providing valuable guidance for robot action learning. Based on this hypothesis, we propose the Video Prediction Policy (VPP), which learns implicit inverse dynamics model conditioned on predicted future representations inside VDMs. To predict more precise future, we fine-tune pre-trained video foundation model on robot datasets along with internet human manipulation data. In experiments, VPP achieves a 18.6\% relative improvement on the Calvin ABC-D generalization benchmark compared to the previous state-of-the-art, and demonstrates a 31.6\% increase in success rates for complex real-world dexterous manipulation tasks. Project page at https://video-prediction-policy.github.io |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14803 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations Hu, Yucheng Guo, Yanjiang Wang, Pengchao Chen, Xiaoyu Wang, Yen-Jen Zhang, Jianke Sreenath, Koushil Lu, Chaochao Chen, Jianyu Computer Vision and Pattern Recognition Robotics Visual representations play a crucial role in developing generalist robotic policies. Previous vision encoders, typically pre-trained with single-image reconstruction or two-image contrastive learning, tend to capture static information, often neglecting the dynamic aspects vital for embodied tasks. Recently, video diffusion models (VDMs) demonstrate the ability to predict future frames and showcase a strong understanding of physical world. We hypothesize that VDMs inherently produce visual representations that encompass both current static information and predicted future dynamics, thereby providing valuable guidance for robot action learning. Based on this hypothesis, we propose the Video Prediction Policy (VPP), which learns implicit inverse dynamics model conditioned on predicted future representations inside VDMs. To predict more precise future, we fine-tune pre-trained video foundation model on robot datasets along with internet human manipulation data. In experiments, VPP achieves a 18.6\% relative improvement on the Calvin ABC-D generalization benchmark compared to the previous state-of-the-art, and demonstrates a 31.6\% increase in success rates for complex real-world dexterous manipulation tasks. Project page at https://video-prediction-policy.github.io |
| title | Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2412.14803 |