Quadrupedal animals can perform agile and playful tasks while interacting with real-world objects. For instance, a trained dog can track and catch a flying frisbee before it touches the ground, while a cat left alone at home may leap to grasp the door handle. Successfully grasping an object during high-dynamic locomotion requires highly precise perception and control. However, due to hardware limitations, agility and precision are usually a trade-off in robotics problems. In this work, we employ a perception-control decoupled system based on Reinforcement Learning (RL), aiming to explore the level of precision a quadrupedal robot can achieve while interacting with objects during high-dynamic locomotion. Our experiments show that our quadrupedal robot, mounted with a passive gripper in front of the robot’s chassis, can perform both tracking and catching tasks similar to a real trained dog. The robot can follow a mid-air ball moving at speeds of up to 3m/s and it can leap and successfully catch a small object hanging above it at a height of 1.05m in simulation and 0.8m in the real world.

This article reviews published research in the field of computer-aided colorization technology. We argue that within this context, the colorization task can be considered to originate from computer graphics, advance by introducing computer vision, and progress towards the fusion of vision and graphics. Hence, we propose a specific taxonomy and organize the research work chronologically. We extend the existing reconstruction-based colorization evaluation techniques on the basis that aesthetic assessment should be introduced to ensure the computer-coloredimages closely satisfy human visual-related requirements. We then perform an aesthetic assessment using the proposed metric and existing evaluations, comparing the colorization performance of seven representative unconditional colorization models. Finally, we identify unresolved issues and propose fruitful areas for future research and development.

Abstract Significant advancements have been made in colorization in recent years, especially with the introduction of deep learning technology. However, challenges remain in accurately colorizing images under certain lighting conditions, such as shadow. Shadows often cause distortions and inaccuracies in object recognition and visual data interpretation, impacting the reliability and effectiveness of colorization techniques. These problems often lead to unsaturated colors in shadowed images and incorrect colorization of shadows as objects. Our research proposes the first shadow-aware image colorization method, addressing two key challenges that previous studies have overlooked: integrating shadow information with general semantic understanding and preserving saturated colors while accurately colorizing shadow areas. To tackle these challenges, we develop a dual-branch shadow-aware colorization network. Additionally, we introduce our shadow-aware block, an innovative mechanism that seamlessly integrates shadow-specific information into the colorization process, distinguishing between shadow and non-shadow areas. This research significantly improves the accuracy and realism of image colorization, particularly in shadow scenarios, thereby enhancing the practical application of colorization in real-world scenarios.

A better understanding of wildfire spreading processes is expected both for long-term wildfire management planning and effective suppression strategies so that wildfires-caused losses in natural resources, endangered species, human lives and properties can be reduced. The cellular automaton (CA) method is useful and powerful for modeling natural and artificial systems in which the constituent parts are locally and tightly interacted. The temporal and spatial characteristics of wildfires make the CA method a preferred choice to model the temporal and spatial variability. In this study, hexagonal tessellation space was used to develop a CA model and then the Rothermers fire spreading model (1972) was integrated to the CA model to simulate wildfire-spreading processes spatially and temporally. The hexagon-based wildfire spreading CA model was verified with the experimental data and also tested using the data obtained in the southern part of Gansu province (China), and the comparison between the simulated results and observed data suggests that this CA-based model can be used to provide useful information for managing wildfires over heterogeneous landscapes.