Collaborative Research: Differentiable and Expressive Simulators for Designing AI-enabled Robots

这个项目开发了一种新颖的原则性和系统性的方法来缩小机器人中所谓的模拟到真实的差距。这项工作为未来的机器人技术以及计算机辅助工程带来了突破，为美国的汽车、航空航天和太空探索等行业奠定了基础。因此，这个奖项的成果给社会带来了好处，也增加了美国的竞争力。该项目产生知识并建立算法，使计算机模拟能够减少工程智能和安全机器人的设计时间和成本。人工智能（AI）将赋予下一代机器人行动能力和决策能力。然而，封装机器人与其环境之间物理交互的大量训练数据的实验和获取可能是昂贵的，有时对机器人和从业者来说是有风险的。现有的仿真技术提供了虚拟测试平台，机器人可以在其中高效安全地学习，但有一个主要缺点-通过计算机仿真设计的机器人在部署到现实世界时运行方式不同，通常效率较低，这被称为模拟与真实差距。该奖项支持基础研究，以减少，并在可能的情况下消除模拟到真实的差距，释放人工智能机器人的全部潜力。通过将众所周知的物理方程和从数据中学习到的经验特征相结合，开发的新的建模技术和数值算法能够准确地捕捉现实世界的现象。该项目还推动了机器人技术的发展，并扩大了来自代表性不足群体的高中生对计算机的参与。目前，机器人模拟器主要存在两个问题。首先，使用的机器人模型不够精确，表达能力不够。其次，寻求改进这些模型的方法既费力又针对机器人。本研究旨在创造新一代的表达和可微分模拟器来解决这两个问题。如果模拟器能够捕捉感兴趣的物理，那么它就是富有表现力的。“可微分模拟器”除了预测机器人的时间演变之外，还可以产生梯度信息，从而允许模拟器自动调整以更好地匹配现实世界。研究团队专注于建立数学基础，具有可学习组件的机器人模拟器原型，研究在机器人应用中利用这些模拟器的实际方法，并评估表达和可微分模拟器对缩小模拟与真实差距的影响。该项目由跨部门机器人基础研究项目支持，由工程（ENG）和计算机与信息科学与工程（CISE）联合管理和资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project develops a novel principled and systematic approach to closing the so-called sim-to-real gap in robotics. The work delivers a breakthrough in future robot technologies, as well as Computer Aided Engineering, which anchors US industries such as automotive, aerospace, and space exploration. Therefore, results from this award bring benefits to the society and increase US competitiveness. This project produces knowledge and establishes algorithms that enable computer simulation to reduce design time and costs in engineering intelligent and safe robots. Artificial intelligence (AI) is poised to endow the next generation of robots with mobility and decision-making skills. However, experimentation and acquisition of large amounts of training data encapsulating physical interaction between the robot and its environment can be costly and sometimes risky to the robots and practitioner. Existing simulation techniques provide virtual testbeds in which the robot can learn efficiently and safely, but have one major drawback - robots designed via computer simulation operate differently, often less effectively, when deployed to the real world, which is called the sim-to-real gap. This award supports fundamental research to reduce, and whenever possible eliminate, the sim-to-real gap, unlocking the full potential of AI-enabled robots. The new modeling techniques and numerical algorithms developed are able to accurately capture the phenomena of the real world by combining well-known physics equations and empirical traits learned from data. The project also advances the state of the art in robotic technologies and broaden the participation in computing of high-school students from underrepresented groups. Presently, robotic simulators are hampered by two main problems. First, the robot models used are not accurate and expressive enough. Second, the methods that seek to improve these models are both laborious and robot specific. This research aims to create a new generation of expressive and differentiable simulators to address both problems. A simulator is expressive if it has the ability to capture the physics of interest. A “differentiable simulator” is one that in addition to predicting the time evolution of a robot, can also produce gradient information, thus allowing the simulator to be automatically adjusted to better match the real world. The research team focuses on establishing the mathematical foundation, prototyping robotics simulators with learnable components, investigating practical ways to leverage these simulators in robotic applications, and evaluating the impacts of expressive and differentiable simulators on closing the sim-to-real gap.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.