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Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics

arxiv.org · 142 words · saved by 1 readers

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Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics Learning robust and generalizable world models is crucial for enabling efficient and scalable robotic control in real-world environments. In this work, we introduce a novel framework for learning world models that accurately capture complex, partially observable, and stochastic dynamics. The proposed method employs a dual-autoregressive mechanism and self-supervised training to achieve reliable long-horizon predictions without relying on domain-specific inductive biases, ensuring adaptability across div

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