Modelling realistic human behaviours in simulation is an ongoing challenge that resides between several fields like social sciences, philosophy, and artificial intelligence. Human movement is a special type of behaviour driven by intent (e.g. to get groceries) and the surrounding environment (e.g. curiosity to see new interesting places). Services available online and offline do not normally consider the environment when planning a path, which is decisive especially on a leisure trip. Two novel algorithms have been presented to generate human-like trajectories based on environmental features. The Attraction-Based A* algorithm includes in its computation information from the environmental features meanwhile, the Feature-Based A* algorithm also injects information from the real trajectories in its computation. The human-likeness aspect has been tested by a human expert judging the final generated trajectories as realistic. This paper presents a comparison between the two approaches in some key metrics like efficiency, efficacy, and hyper-parameters sensitivity. We show how, despite generating trajectories that are closer to the real one according to our predefined metrics, the Feature-Based A* algorithm fall short in time efficiency compared to the Attraction-Based A* algorithm, hindering the usability of the model in the real world.
Align Your Rhythm: Generating Highly Aligned Dance Poses with Gating-Enhanced Rhythm-Aware Feature Representation
College of Computer Science and Technology, Harbin Engineering University（哈尔滨工程大学计算机科学与技术学院）；Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；State Key Laboratory of Space-Ground Integrated Information Technology（空间地面一体化信息技术创新实验室）
Generating and Customizing Robotic Arm Trajectories using Neural Networks
Faculty of Mathematics, Physics and Informatics（数学、物理与信息学系）；Comenius University Bratislava（布拉迪斯拉发康门尼大学）；DIBRIS（迪布里斯）；University of Genoa（热那亚大学）；Italian Institute of Technology（意大利理工学院）
Benchmarking Reinforcement Learning via Stochastic Converse Optimality: Generating Systems with Known Optimal Policies
Skolkovo Institute of Science and Technology（斯克尔科沃科学与技术研究所）；Innopolis University（因纳波利斯大学）；Technische Universität Chemnitz（化学工业大学）；Central University（中央大学）；Sirius University of Science and Technology（西里乌斯科学与技术大学）
