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SoccerDiffusion: Toward Learning End-to-End Humanoid Robot Soccer from Gameplay Recordings

Created by
  • Haebom

Author

Florian Vahl, J orn Griepenburg, Jan Gutsche, Jasper G uldenstein, Jianwei Zhang

Outline

SoccerDiffusion is a Transformer-based diffusion model that learns end-to-end control policies for humanoid robot soccer using recorded data from real RoboCup games. It predicts joint command sequences from a variety of sensor inputs, including vision, proprioception, and game state. It reduces the multi-step diffusion process to a single step using knowledge distillation techniques for real-time inference. It demonstrates the ability to replicate complex behaviors such as walking, kicking, and fall recovery on both simulations and real robots. Although its high-dimensional tactical behaviors are limited, it provides a solid foundation for future reinforcement learning or preference optimization methods. The dataset, pretrained models, and code are publicly available at https://bit-bots.github.io/SoccerDiffusion .

Takeaways, Limitations

Takeaways:
Presenting the possibility of learning humanoid robot soccer control policy using actual RoboCup data.
Implementing real-time inference through knowledge distillation.
Successful reproduction of complex movements such as walking, kicking, and recovering from falls.
Ensuring research reproducibility and scalability through disclosure of datasets, models, and code.
Limitations:
Limited performance for high-level tactical actions.
Further research is needed for higher-order tactical actions.
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