SoccerDiffusion: Toward Learning End-to-End Humanoid Robot Soccer from Gameplay Recordings
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908431015739392 |
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| author | Vahl, Florian Griepenburg, Jörn Gutsche, Jan Güldenstein, Jasper Zhang, Jianwei |
| author_facet | Vahl, Florian Griepenburg, Jörn Gutsche, Jan Güldenstein, Jasper Zhang, Jianwei |
| contents | This paper introduces SoccerDiffusion, a transformer-based diffusion model designed to learn end-to-end control policies for humanoid robot soccer directly from real-world gameplay recordings. Using data collected from RoboCup competitions, the model predicts joint command trajectories from multi-modal sensor inputs, including vision, proprioception, and game state. We employ a distillation technique to enable real-time inference on embedded platforms that reduces the multi-step diffusion process to a single step. Our results demonstrate the model's ability to replicate complex motion behaviors such as walking, kicking, and fall recovery both in simulation and on physical robots. Although high-level tactical behavior remains limited, this work provides a robust foundation for subsequent reinforcement learning or preference optimization methods. We release the dataset, pretrained models, and code under: https://bit-bots.github.io/SoccerDiffusion |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20808 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SoccerDiffusion: Toward Learning End-to-End Humanoid Robot Soccer from Gameplay Recordings Vahl, Florian Griepenburg, Jörn Gutsche, Jan Güldenstein, Jasper Zhang, Jianwei Robotics Artificial Intelligence Machine Learning This paper introduces SoccerDiffusion, a transformer-based diffusion model designed to learn end-to-end control policies for humanoid robot soccer directly from real-world gameplay recordings. Using data collected from RoboCup competitions, the model predicts joint command trajectories from multi-modal sensor inputs, including vision, proprioception, and game state. We employ a distillation technique to enable real-time inference on embedded platforms that reduces the multi-step diffusion process to a single step. Our results demonstrate the model's ability to replicate complex motion behaviors such as walking, kicking, and fall recovery both in simulation and on physical robots. Although high-level tactical behavior remains limited, this work provides a robust foundation for subsequent reinforcement learning or preference optimization methods. We release the dataset, pretrained models, and code under: https://bit-bots.github.io/SoccerDiffusion |
| title | SoccerDiffusion: Toward Learning End-to-End Humanoid Robot Soccer from Gameplay Recordings |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2504.20808 |