机器人世界模型：用于机器人领域鲁棒策略优化的神经网络模拟器
Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics
Authors
Chenhao Li, Andreas Krause, Marco Hutter
学习鲁棒且具有泛化能力的世界模型，对于在真实环境中实现高效可扩展的机器人控制至关重要。本研究提出一种新型世界模型学习框架，能够精确捕捉复杂、部分可观测且具有随机性的动态特性。该方法通过双自回归机制与自监督训练，在无需依赖特定领域归纳偏好的前提下实现可靠的长时序预测，确保在多样化机器人任务中的适应性。我们进一步提出基于世界模型的策略优化框架，可在虚拟环境中高效训练并无缝部署至真实系统。本研究通过解决长时域预测、误差累积及仿真到现实迁移等难题，推动了基于模型的强化学习发展。所提出方法构建了可扩展且稳健的框架，为现实应用中自适应高效的机器人系统铺平道路。
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.
