用神经网络模拟机器人世界模型，提升复杂环境下的控制效率。
Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics
- 采用双自回归机制与自监督训练，捕捉部分可观测的随机动态
- 实现无需领域先验的长时程可靠预测，误差累积显著降低
- 适合需要高适应性的真实机器人任务，如工业自动化
学习鲁棒且可泛化的世界模型对于实现在真实环境中高效、可扩展的机器人控制至关重要。本文提出一种新框架，用于学习能准确捕捉复杂、部分可观测和随机动力学的世界模型。该方法采用双自回归机制和自监督训练，实现无需依赖特定领域先验的可靠长时程预测，确保在多种机器人任务中具备适应性。我们进一步提出一个利用世界模型进行想象环境训练并无缝部署于真实系统中的策略优化框架。本工作通过解决长时程预测、误差累积和仿真到现实的迁移挑战，推进了基于模型的强化学习的发展。所提方法构建了一个可扩展且鲁棒的框架，为真实应用中的自适应、高效机器人系统铺平道路。
原文摘要 · Abstract (English)
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.
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