Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics
This work addresses the challenge of enabling efficient and scalable robotic control in real-world environments, representing an incremental improvement in model-based reinforcement learning.
The paper tackles the problem of learning robust world models for robotic control by introducing a novel framework that uses a dual-autoregressive mechanism and self-supervised training to achieve reliable long-horizon predictions, advancing model-based reinforcement learning for real-world deployment.
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. This work advances model-based reinforcement learning by addressing the challenges of long-horizon prediction, error accumulation, and sim-to-real transfer. By providing a scalable and robust framework, the introduced methods pave the way for adaptive and efficient robotic systems in real-world applications.