CAREER: Concurrent Robot Learning from Simulation and Real for Closing the Sim-to-real Gap

像机器狗或人形机器人一样，腿部机器人提供了在室内和室外任务中很好地移动的可能性，比如重新布置家里的家具或监控工厂。然而，控制这些机器人是困难的，并伴随着机器人坠落的风险。近年来，人工智能(AI)通过在教机器人如何行走时使用计算机模拟来应对不断下降的挑战，显示出了良好的前景。然而，由于仿真与真实物理机器人之间的差异，在仿真中学习到的机器人行为在现实世界中往往表现不佳。该学院早期职业发展(Career)项目支持一种同时从模拟和物理实验中学习的新方法的研究。该项目中的这种新颖方法旨在利用模拟的优势，例如可伸缩性，同时使用来自真实机器人的数据。最终，该项目将最大限度地发挥人工智能和机器人学习算法的潜力，以允许更有能力和更安全的机器人。此外，该项目还包括教育活动，让学生接触真实世界的机器人学习环境，促进对机器人的更广泛理解。本项目将调查一类新的学习算法，从多元宇宙学习(LfM)，该算法同时从大规模、廉价的基于物理的模拟和昂贵的真实世界地面事实中学习，以弥合腿部机器人众所周知的“模拟到现实”的差距。该项目的基本原则包括从模拟和真实经验中无缝和连续地学习，以及对实验数据进行结构化的数学推理。这种方法不同于现有的大多数单纯依赖于模拟或真实世界经验的学习算法。这项研究涉及三个主要组成部分的开发：(I)真实世界中的自主和安全的学习环境，(Ii)同时利用模拟和现实的新型机器人学习算法，以及(Iii)可解释的人工智能以理解硬件实验。该项目将促进安全关键型腿部机器人的挑战性运动技能的发展，例如带有机械手和两足动物的四足机器人，同时确保有效、高效和安全的机器人学习。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Legged robots, like robot dogs or humanoid robots, offer the possibility of being able to move well in indoor and outdoor tasks, such as rearranging furniture at home or monitoring factories. However, controlling these robots is difficult and comes with the risk of robots falling. In recent years, artificial intelligence (AI) has shown promise in addressing the falling challenge by using computer simulations when teaching robots how to walk. However, the robot behaviors learned in simulation often underperform in the real world due to the difference between the simulation and the real physical robot. This Faculty Early Career Development (CAREER) project supports research that investigates a novel approach that simultaneously learns from both simulated and physical experiments. This novel approach in this project aims to leverage the advantage of simulations, such as scalability, while using the data from real robots. Ultimately, the project will maximize the potential of AI and robot learning algorithms to allow more capable and safer robots. Additionally, the project incorporates educational activities to engage students with real-world robot learning environments, fostering a broader understanding of robotics.This project will investigate a novel class of learning algorithms, Learning from Multiverse (LfM), which concurrently learns from both large-scale, inexpensive physics-based simulation and expensive, real world ground-truths to bridge the well-known “sim-to-real” gap in legged robots. The fundamental principle of the project involves seamless and continuous learning from both simulated and real experiences, as well as structured mathematical reasoning of experimental data. This approach differs from the majority of existing learning algorithms that rely solely on either simulation or real-world experience. This research involves the development of three main components: (i) autonomous and safe learning environments in the real world, (ii) novel robot learning algorithms that simultaneously leverage simulation and reality, and (iii) explainable artificial intelligence to understand hardware experiments. This project will facilitate the development of challenging motor skills for safety-critical legged robots, such as quadrupedal robots with manipulators and bipeds, while ensuring effective, efficient, and safe robot learning.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.