The economics of Physical AI: Why data quality beats scale
为了达到物理 AI 社区所期望的鲁棒性水平，即在不熟悉的环境中对不熟悉的对象进行零样本部署的通才策略，数据集大小必须增长几个数量级。为了给出规模感，将逻辑扩展到 LLM 规模的数据量（大约 10^2）将需要大约 8000 万个机器人连续运行三年。 The field is therefore bottlenecked not only by compute or model architecture, but more fundamentally by the rate at which high-quality, real-world manipulation data can be gen
IEEE Transactions on Neural Networks and Learning Systems, Volume 37, Issue 5, May 2026
1) Deep Model Fusion: A Survey 作者：W. Li, Y. Peng, M. Zhang, L. Ding, H. Hu, L. ShenPages: 2008 - 20242) Survey on Efficient Large Language Model:principles, Algorithms, Applications, and Open Issues作者：J. Cheng, H. Kang, Y. Shao, N. Li, P. Chen, R. Wang, S. Long, X. Yang, L. 页数: 2025 - 20453) 基于骨架的动作
