Robotic task planning in real-world environments requires not only object recognition but also a nuanced understanding of spatial relationships between objects. We present a spatial-relationship-aware dataset of nearly 1,000 robot-acquired indoor images, annotated with object attributes, positions, and detailed spatial relationships. Captured using a Boston Dynamics Spot robot and labelled with a custom annotation tool, the dataset reflects complex scenarios with similar or identical objects and intricate spatial arrangements. We benchmark six state-of-the-art scene-graph generation models on this dataset, analysing their inference speed and relational accuracy. Our results highlight significant differences in model performance and demonstrate that integrating explicit spatial relationships into foundation models, such as ChatGPT 4o, substantially improves their ability to generate executable, spatially-aware plans for robotics. The dataset and annotation tool are publicly available at https://github.com/PengPaulWang/SpatialAwareRobotDataset, supporting further research in spatial reasoning for robotics.

Diffusion models have marked a significant mile-stone in the enhancement of image and video generation technologies. However, generating videos that precisely retain the shape and location of moving objects such as robots remains a challenge. This paper presents diffusion models specifically tailored to generate videos that accurately maintain the shape and location of mobile robots. The proposed models incorporate techniques such as embedding accessible robot pose information and applying semantic mask regulation within the scalable and efficient ConvNext backbone network. These techniques are designed to refine intermediate outputs, therefore improving the retention performance of shape and location. Through extensive experimentation, our models have demonstrated notable improvements in maintaining the shape and location of different robots, as well as enhancing overall video generation quality, compared to the benchmark diffusion model. Codes will be open-sourced at: https://github.com/PengPaulWang/diffusion-robots.

This paper proposes a computer vision framework aimed to segment hot steel sections and contribute to rolling precision. The steel section dimensions are calculated for the purposes of automating a high temperature rolling process. A structured forest algorithm along with the developed steel bar edge detection and regression algorithms extract the edges of the high temperature bars in optical videos captured by a GoPro® camera. To quantify the impact of noises that affect the segmentation process and the final diameter measurements, a weighted variance is calculated, providing a level of trust in the measurements. The results show an accuracy which is in line with the rolling standards, i.e. with a root mean square error less than 2.5 mm.

Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applications such as autonomous driving and hazardous weather forecasting. However, approaches for theoretical analysis of Bayesian neural networks remain limited. This paper makes a step forward towards mathematical quantification of uncertainty in neural network models and proposes a cubature-rule-based computationally-efficient uncertainty quantification approach that captures layer-wise uncertainties of Bayesian neural networks. The proposed approach approximates the first two moments of the posterior distribution of the parameters by propagating cubature points across the network nonlinearities. Simulation results show that the proposed approach can achieve more diverse layer-wise uncertainty quantification results of neural networks with a fast convergence rate.

Deep neural networks have recently demonstrated the traffic prediction capability with the time series data obtained by sensors mounted on road segments. However, capturing spatio-temporal features of the traffic data often requires a significant number of parameters to train, increasing computational burden. In this work we demonstrate that embedding topological information of the road network improves the process of learning traffic features. We use a graph of a vehicular road network with recurrent neural networks (RNNs) to infer the interaction between adjacent road segments as well as the temporal dynamics. The topology of the road network is converted into a spatio-temporal graph to form a structural RNN (SRNN). The proposed approach is validated over traffic speed data from the road network of the city of Santander in Spain. The experiment shows that the graph-based method outperforms the state-of-the-art methods based on spatio-temporal images, requiring much fewer parameters to train.

This paper proposes a set-membership based method for simultaneous localization and mapping. A box particle filter is exploited and improved to estimate robot states and feature positions. An interval constraint propagation is used to reduce box sizes, i.e., to decrease the uncertainty of the estimates. Buffers are also used to get q-satisfied results when empty estimates arise, on the one hand. On the other hand, historical data are used to improve the estimation through buffer contraction. Illustrations of the proposed method are given over simulations and experiments, with comparisons with a particle filter based method. The results show that the proposed method can reach the same simultaneous localization and mapping accuracy as a particle filter based method but with fewer particles. Moreover, this approach is comparatively more robust to system and measurement noises.