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

Understand

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 diverse robotic tasks.
• We further propose a policy optimization framework that leverages world models for efficient training in imagined environments and seamless deployment in real-world systems.