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

• URL: http://arxiv.org/abs/2501.10100v1
• Date: Fri, 17 Jan 2025 10:39:09 GMT
• Title: Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics
• Authors: Chenhao Li, Andreas Krause, Marco Hutter,
• Abstract summary: We introduce a novel framework for learning world models. By providing a scalable and robust framework, we pave the way for adaptive and efficient robotic systems in real-world applications.
• Score: 50.191655141020505
• License:
• Abstract: 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. Through extensive experiments, our approach consistently outperforms state-of-the-art methods, demonstrating superior autoregressive prediction accuracy, robustness to noise, and generalization across manipulation and locomotion tasks. Notably, policies trained with our method are successfully deployed on ANYmal D hardware in a zero-shot transfer, achieving robust performance with minimal sim-to-real performance loss. 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.

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