Abstract Video prediction is the process of learning necessary information from historical frames to predict future video frames. Learning features from historical frames is a crucial step in this process. However, most current methods have a relatively single‐scale learning approach, even if they learn features at different scales, they cannot fully integrate and utilise them, resulting in unsatisfactory prediction results. To address this issue, a hierarchical multi‐scale fusion unit (HMSFU) is proposed. By using a hierarchical multi‐scale architecture, each layer predicts future frames at different granularities using different convolutional scales. The abstract features from different layers can be fused, enabling the model not only to capture rich contextual information but also to expand the model's receptive field, enhance its expressive power, and improve its applicability to complex prediction scenarios. To fully utilise the expanded receptive field, HMSFU incorporates three fusion modules. The first module is the single‐layer historical attention fusion module, which uses an attention mechanism to fuse the features from historical frames into the current frame at each layer. The second module is the single‐layer spatiotemporal fusion module, which fuses complementary temporal and spatial features at each layer. The third module is the multi‐layer spatiotemporal fusion module, which fuses spatiotemporal features from different layers. Additionally, the authors not only focus on the frame‐level error using mean squared error loss, but also introduce the novel use of Kullback–Leibler (KL) divergence to consider inter‐frame variations. Experimental results demonstrate that our proposed HMSFU model achieves the best performance on popular video prediction datasets, showcasing its remarkable competitiveness in the field.

This article proposes two decentralized multiagent optimal control methods that combine the computational efficiency and scalability of differential dynamic programming (DDP) and the distributed nature of the alternating direction method of multipliers (ADMM). The first one, nested distributed DDP, is a three-level architecture, which employs ADMM for consensus, an augmented Lagrangian layer for local constraints and DDP as the local optimizer. The second one, merged distributed DDP, is a two-level architecture that addresses both consensus and local constraints with ADMM, further reducing computational complexity. Both frameworks arefully decentralizedsince all computations are parallelizable among the agents and only local communication is necessary. Simulation results that scale up to thousands of cars and hundreds of drones demonstrate the effectiveness of the algorithms. Superior scalability to large-scale systems against other DDP and sequential quadratic programming methods is also illustrated. Finally, hardware experiments on a multirobot platform verify the applicability of the methods. A video with all results is provided in the supplementary material.