As autonomous driving systems being deployed to millions of vehicles, there is a pressing need of improving the system's scalability, safety and reducing the engineering cost. A realistic, scalable, and practical simulator of the driving world is highly desired. In this paper, we present an efficient solution based on generative models which learns the dynamics of the driving scenes. With this model, we can not only simulate the diverse futures of a given driving scenario but also generate a variety of driving scenarios conditioned on various prompts. Our innovative design allows the model to operate in both full-Autoregressive and partial-Autoregressive modes, significantly improving inference and training speed without sacrificing generative capability. This efficiency makes it ideal for being used as an online reactive environment for reinforcement learning, an evaluator for planning policies, and a high-fidelity simulator for testing. We evaluated our model against two real-world datasets: the Waymo motion dataset and the nuPlan dataset. On the simulation realism and scene generation benchmark, our model achieves the state-of-the-art performance. And in the planning benchmarks, our planner outperforms the prior arts. We conclude that the proposed generative model may serve as a foundation for a variety of motion planning tasks, including data generation, simulation, planning, and online training. Source code is public at https://github.com/HorizonRobotics/GUMP/
Configuration Space Decomposition for Scalable Proxy Collision Checking in Robot Planning and Control
University of California San Diego（加州大学圣地亚哥分校）
复杂高维环境下机器人的实时运动规划是一个亟待解决的问题。运动规划算法及其底层碰撞检查器对于任何机器人控制堆栈都至关重要。在机器人运动规划中，碰撞检测占据了很大一部分计算时间。现有的碰撞检查器在速度和精度之间进行权衡，并且对于高维复杂环境的扩展性很差。我们提出了一种新的空间分解方法，使用K-Means聚类的正运动学空间加速代理碰撞检查。我们训练个人配置空间模型使用Fastron，一个核心感知器算法，在这些分解的子空间，产生紧凑而高度准确的模型，可以快速查询和更好地扩展到更复杂的环境。我们在7自由度Baxter机器人上演示了这种名为Decomposed Fast Perceptron（D-Fastron）的新方法，与最先进的几何碰撞检查器相比，碰撞检查速度平均快29倍，运动规划速度快9.8倍。
Generative Multi-Robot Motion Planning via Diffusion Modeling with Multi-Agent Reinforcement Learning Guidance
School of Manufacturing Systems and Networks, Arizona State University, Mesa, AZ（1制造系统与网络学院，亚利桑那州立大学，梅萨，AZ）；Michael W. Hall School of Mechanical Engineering, Mississippi State University, Starkville, MS（2迈克尔·W·霍尔机械工程学院，密西西比州立大学，斯塔克维尔，MS）
XFlowMP: Task-Conditioned Motion Fields for Generative Robot Planning with Schrodinger Bridges
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（莫扎德·本·扎耶德人工智能大学）；Automation & Control Institute (ACIN), TU Wien（自动化与控制研究所）；Austrian Institute of Technology (AIT) GmbH（奥地利技术研究所）
Conditional Generative Adversarial Networks for Optimal Path Planning
Southern University of Science and Technology（南方科技大学）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen Research Institute of the Chinese University of Hong Kong（香港中文大学深圳研究院）
Parallel OctoMapping: A Scalable Framework for Enhanced Path Planning in Autonomous Navigation
Department of Mechanical and Aerospace Engineering, University of Florida（佛罗里达大学机械与航空航天工程系）；Department of Electrical and Systems Engineering, University of Pennsylvania（宾夕法尼亚大学电气与系统工程系）
Efficient Heuristic Generation for Robot Path Planning with Recurrent Generative Model
Southern University of Science and Technology（南方科技大学）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen Research Institute of the Chinese University of Hong Kong（香港中文大学深圳研究院）
Generative Adversarial Network based Heuristics for Sampling-based Path Planning
Southern University of Science and Technology（南方科技大学）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen Research Institute of the Chinese University of Hong Kong（香港中文大学深圳研究院）
Generative Planning with 3D-vision Language Pre-training for End-to-End Autonomous Driving
College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；COWAROBOT（酷哇机器人）；School of Electronic Engineering, University of South China（南华大学电子工程学院）
Robust Maneuver Planning With Scalable Prediction Horizons: A Move Blocking Approach
Institute of Flight Systems, German Aerospace Center (DLR)（德国航空航天中心飞行系统研究所）；Institute for Production Technology and Systems, Leuphana University of Lueneburg（吕讷堡勒法纳大学生产技术与系统研究所）
City-Wide Low-Altitude Urban Air Mobility: A Scalable Global Path Planning Approach via Risk-Aware Multi-Scale Cell Decomposition
School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）；School of Aeronautics and Astronautics, Purdue University（普渡大学航空航天学院）
Structural Induced Exploration for Balanced and Scalable Multi-Robot Path Planning
Department of Artificial Intelligence, School of Electronics Engineering, Kyungpook National University（人工智能系，电子工程学院，韩国庆北国立大学）；Department of IT Convergence Engineering, Kumoh National Institute of Technology（IT融合工程系，木谷国立技术学院）
SMART: Scalable Multi-Agent Reasoning and Trajectory Planning in Dense Environments
Department of Civil and Environmental Engineering University of Wisconsin-Madison（威斯康星大学麦迪逊分校土木与环境工程系）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与移动性学院）；Department of Civil Engineering, The University of Hong Kong（香港大学土木工程系）
APTree: Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Construction
University of Stuttgart（斯图加特大学）；Institute for Computational Design and Construction（计算设计与建造研究所）；Institute for Control Engineering of Machine Tools and Manufacturing Units（机床与制造单元控制工程研究所）
