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Sim-to-real transfer of world model for manipulation tasks.
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2026-02-17 10:30:50
raw text
TWIST: Teacher-Student World Model Distillation for Efficient Sim-to-Real Transfer TWIST: Teacher-Student World Model Distillation for Efficient Sim-to-Real Transfer Jun Yamada , Marc Rigter , Jack Collins , Ingmar Posner , University of Oxford IEEE International Conference on Robotics and Automation (ICRA), 2024 Paper arXiv Code (Coming soon) Abstract Model-based RL is a promising approach for real-world robotics due to its improved sample efficiency and generalization capabilities compared to model-free RL. However, effective model-based RL solutions for vision-based real-world applications require bridging the sim-to-real gap for any world model learnt. Due to its significant computational cost, standard domain randomisation does not provide an effective solution to this problem. This paper proposes TWIST (Teacher-Student World Model Distillation for Sim-to-Real Transfer) to achieve efficient sim-to-real transfer of vision-based model-based RL u...
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