Physically based simulation: learning from and interacting with reality

基于物理的模拟：从现实中学习并与之互动

• 批准号: RGPIN-2018-05665
• 负责人: Kry, Paul
• 金额: $ 5.97万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750365
• 关键词: Physically; based; simulation; learning; interacting

Over the last 10 years I have worked on physically based simulation for computer animation. This includes simulated control of humans for locomotion and grasping, physically inspired motion editing tools, models for collision, contact, deformation, and more recently 3D printed interfaces and mechanisms. While incredible progress has been made in the last decade, there are likewise many new opportunities and important challenges. The objective of this research program is the development of new models, formulations, and simulation techniques that better match the behaviour of real world examples, with the ultimate goal of using these models in computational design for fabrication, control, and interaction with the real world. Accurate and efficient compliant contact, and learning data driven models for friction and deformation can benefit computational design of 3D printed mechanisms. Furthermore, learning hand manipulation strategies from capture for the creation of dexterous controllers is an ongoing challenge in both simulation and robotics. As a long term objective, this research program aims to help bridge the reality gap that exists between computer simulation and the actual behaviour of materials, mechanisms, and robots in the living world. The general approach I take in my research is to (1) identify and capture the subtleties of the real world where possible, (2) design models that effectively approximate the relevant details, and (3) develop efficient methods for simulating complex interactions with these natural phenomena. In the next years of my research program, I also plan to exploit recent advances that have been made in machine learning. Function approximation techniques can help capture the true behaviour of materials, and deep networks appear to be promising for dexterous manipulation given how they have successfully advanced the state of the art in the domain of locomotion control. The products of my research program will offer both minor adjustments as well as fundamentally different approaches to physics based simulation. The results lead to applications in entertainment, for instance, character animation for next generation video games, movies, television, and new media. There are also important applications in training simulations, ergonomics, robotics, e-commerce, and rehabilitation.

在过去的10年里，我一直致力于计算机动画的物理模拟。这包括用于运动和抓取的模拟人类控制、物理启发的运动编辑工具、用于碰撞、接触、变形的模型，以及最近的3D打印界面和机制。在过去十年取得令人难以置信的进展的同时，同样也有许多新的机会和重要的挑战。该研究计划的目标是开发新的模型、公式和模拟技术，以更好地匹配真实世界示例的行为，最终目标是在制造、控制和与真实世界交互的计算设计中使用这些模型。准确高效的柔顺接触，学习数据驱动的摩擦和变形模型，有助于三维打印机构的计算设计。此外，从捕获中学习手操作策略以创建灵活的控制器是仿真和机器人学中的一个持续挑战。作为一个长期目标，这项研究计划旨在帮助弥合计算机模拟与现实世界中材料、机械和机器人的实际行为之间存在的现实差距。在我的研究中，我采取的一般方法是：(1)尽可能地识别和捕捉现实世界的微妙之处，(2)设计有效地近似相关细节的模型，以及(3)开发有效的方法来模拟与这些自然现象的复杂互动。在我未来几年的研究计划中，我还计划利用最近在机器学习方面取得的进展。函数逼近技术可以帮助捕捉材料的真实行为，鉴于深度网络如何成功地推动了运动控制领域的最新技术，深网络似乎有望实现灵活的操作。我的研究计划的产品将为基于物理的模拟提供微小的调整以及根本不同的方法。这些结果导致了在娱乐领域的应用，例如，下一代视频游戏、电影、电视和新媒体的角色动画。在训练模拟、人体工程学、机器人、电子商务和康复方面也有重要的应用。