TLDR
人工智能的快速进展正在重塑机器人领域，并加速学习类方法的落地。尽管纯数据驱动方法在计算机视觉与自然语言处理中已取得显著成效，但机器人领域仍受限于数据稀缺、真实环境交互复杂以及运行可靠性要求高等问题。这些挑战推动了"物理嵌入的机器人学习"探索，即将物理先验嵌入到学习算法中。通过编码底层物理规律与约束，物理先验可在数据有限的情况下进行补充The rapid progress of artificial intelligence is reshaping robotics and accelerating the adoption of learning-based approaches. While purely data-driven methods have achieved remarkable success in computer vision and natural language processing, robotics remains constrained by limited data, complex real-world interactions, and the need for reliable operation. These challenges have motivated the exploration of physics-embedded robot learning, which embeds physics priors into learning algorithms. By encoding the underlying physical laws and constraints, physics priors can complement limited data