Recent advances in foundation models have shown promising results in developing generalist robotics that can perform diverse tasks in open-ended scenarios given multimodal inputs. However, current work has been mainly focused on indoor, household scenarios. In this work, we present RoboScape, a simulation platform for embodied AI in large-scale, photorealistic urban environments. Built on Unreal Engine 5, RoboScape procedurally generates unlimited photorealistic urban scenes populated with dynamic elements such as pedestrians and traffic systems, surpassing prior urban simulations in realism, complexity, and scalability. It also supports multi-robot control and communication. With these key features, we build two challenging robot benchmarks: (1) a multimodal instruction-following task, where a robot must follow vision-language navigation instructions to reach a destination in the presence of pedestrians and traffic; and (2) a multi-agent search task, where two robots must communicate to cooperatively locate and meet each other. Unlike existing benchmarks, these two new benchmarks comprehensively evaluate a wide range of critical robot capacities in realistic scenarios, including (1) multimodal instructions grounding, (2) 3D spatial reasoning in large environments, (3) safe, long-range navigation with people and traffic, (4) multi-robot collaboration, and (5) grounded communication. Our experimental results demonstrate that state-of-the-art models, including vision-language models (VLMs), struggle with our tasks, lacking robust perception, reasoning, and planning abilities necessary for urban environments.

Owing to powerful generative priors, Text-to-Image (T2I) diffusion models have achieved promising results in image colorization task. However, recent advanced methods primarily integrate global semantics. Such practice neglects local semantics, yielding suboptimal colorization performance. In this paper, we present a novel global-local semantic aware colorization method named GoLoColor, which performs semantic awareness at both global and local levels. The GoLoColor includes Global Aware (GoA) module, Local Aware module (LoA) and Semantic Aggregation (SA) module for semantic understanding. Specifically, the GoA produces global semantic embedding to represent whole image, while the LoA provides semantic support for local objects, particularly in scenes containing multiple entities. The SA module facilitates semantic interaction between local and global semantic embedding to produce richer semantic information. Finally, a controlled T2I diffusion model is utilized to produce color image guided by the aggregated semantic embedding. Comprehensive experiments demonstrate that our method achieves superior performance and can produce realistic colorization.

Graph Neural Networks (GNNs) have demonstrated remarkable potential in processing graphstructured data and have been widely adopted for various graph-related tasks. In recent years, graph knowledge distillation has emerged as an effective model optimization technique, achieving outstanding performance in enhancing GNN capabilities. However, it remains constrained by the inherent sparsity and dependency of graph data. To address this, we propose Mixup-driven Distillation for Graph Neural Networks (MD-GNN), an innovative framework that combines Mixup-based data augmentation with knowledge distillation to boost GNN performance. Specifically, we first leverages Mixup to perform linear interpolation on both node features and labels, generating diversified training samples while preserving original graph topology. Subsequently, based on these generated data, we extract knowledge from a pre-trained GNN teacher through output logit alignment to guide the training of student GNN models. Extensive experiments demonstrate that MD-GNN significantly outperforms existing baseline GNN models, achieving performance gains of 2.54%-3.73% on three benchmark datasets (Cora, CiteSeer, and PubMed). Notably, MD-GNN exhibits superior generalization capabilities and enhanced robustness, particularly showing notable advantages in noisy scenarios.