CAREER: Physical Object Modeling for Intelligent Systems

职业：智能系统的物理对象建模

• 批准号: 2338203
• 负责人: Jiajun Wu
• 金额: $ 59.47万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2029-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2338203&HistoricalAwards=false
• 关键词: CAREER; Physical; Object; Modeling; Intelligent

Recent advancements in artificial intelligence (AI) and computer vision have shown the possibility of building machines that can see and explain what is in an image or video. However, for machines to physically interact with the world just like humans, they need to understand more than “what is where” but also that the world contains physical objects with distinct properties, such as their material, shape, density, and multi-sensory characteristics (how they sound and feel). This problem of physical object modeling remains largely ignored in current AI systems. This project aims to introduce systems and methods that may allow us to capture and model diverse, multi-sensory, physical objects to help intelligent machines better understand and interact with the world. Researchers will integrate findings from this research into course development, student advising, and community infrastructure development; they will also partner with educational and non-profit organizations to teach AI, vision, graphics, and robotics to underrepresented students.In this project, investigators will focus on the modeling and extensive applications of multi-sensory physical objects. At the core of the technical plan is a physics-based object representation, which encodes physics-based, object-centric priors in the world, including the notion of object texture, material, geometry, articulation, physical properties, and multi-sensory characteristics. Centered around such representations, the team of researchers will develop (1) new systems that exploit integrated hardware and learning to capture datasets of physical objects; (2) new methods that leverage these objects for computer vision, graphics, and robotics tasks, such as multi-sensory perception and reconstruction, object and scene generation and editing, dynamics modeling and simulation, and robotic interaction and manipulation; (3) new paradigms that study the neurocognitive bases of physical object perception and interaction, with the long-term potential of providing intelligent machines with more human-like capabilities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人工智能（AI）和计算机视觉的最新进步表明，可以看到并解释图像或视频中的内容的建筑机器的可能性。但是，要使机器像人类一样与世界进行物理互动，他们需要比“什么地方”了解更多的理解，但世界包含具有不同属性的物理对象，例如它们的材料，形状，密度和多感官特征（声音和感觉）。物理对象建模的问题在当前的AI系统中仍在很大程度上被忽略。该项目旨在引入系统和方法，以使我们能够捕获和建模潜水员，多感应的物理对象，以帮助智能机器更好地理解和与世界互动。研究人员将将这项研究的发现纳入课程发展，学生咨询和社区基础设施发展；他们还将与教育和非营利组织合作，向代表性不足的学生教授AI，远见，图形和机器人技术。在该项目中，调查人员将专注于多感觉物理对象的建模和广泛应用。技术计划的核心是一种基于物理的对象表示，它在世界上编码基于物理的，以物理为中心的先验，包括对象纹理，材料，几何，几何，表达，物理特性和多感官特征的概念。研究人员团队将围绕此类表示形式开发（1）新系统，这些系统利用集成的硬件并学习捕获物理对象的数据集； （2）将这些对象用于计算机视觉，图形和机器人技术任务的新方法，例如多感知感知和重建，对象和场景生成以及编辑，动态建模和仿真以及机器人交互和操作； （3）研究物理对象感知和互动的神经认知基础的新范式，其长期潜力是为智能机器提供更类似人类的能力。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛的影响审查审查标准来通过评估来通过评估来支持的。