Safe and Efficient Robot Learning in Human-Centric Environments

以人为本的环境中安全高效的机器人学习

• 批准号: RGPIN-2021-04152
• 负责人: Schoellig, Angela
• 金额: $ 4.01万
• 依托单位: University of Toronto
• 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=743280
• 关键词: Safe; Efficient; Robot; Learning; Human

Robotics is poised to revolutionize several aspects of our society in the coming years from the way we move people and things around to healthcare to manufacturing. In particular, mobile robotics technology has matured to the point that self-driving cars and unmanned aerial vehicles (a.k.a., drones) are being actively pursued by industry, from small startups to the world's technology giants. While some products will come to market in the next few years, they will not be fully autonomous, meaning they will not operate under all conditions that a human operator can handle. To achieve this, a great deal more research is need. Real applications of mobile robots are challenging because the operating environment (e.g., our roadways, our skies, big warehouses) can be large, cluttered, dynamic, and unknown/uncertain. This means that the key autonomy functions, which include localization, mapping, planning, and control, must address these challenges. Within robotics, we have barely scratched the surface when it comes to making systems that can operate at a human level. This program of research focuses on expanding the operational envelope of robots to complex and human-centric environments (i.e., environments built for and used by humans such as our roads networks, city centers, buildings, etc). The proposal focuses on techniques that enable robots to act safely and make dependable decisions in dynamic and uncertain situations with multiple actors. Prior knowledge is combined with data to achieve fast adaptation to new situations. In particular, we will address (i) fast adaptation to dynamic disturbances, (ii) data-based motion planning, (iii) performance guarantees in uncertain environments, and (iv) interactive scenarios. These are all important elements of developing autonomy for use in human-centric environments, which tend to be less predictable ahead of time and require robots to operate near other dynamic actors. The work as part of this proposal will include fundamental methodological advances, the implementation of novel algorithms in software, and thorough experimental testing in realistic scenarios. We will embed our new autonomy features into the full autonomy stack comprising all the key functions needed to operate mobile robots through a dynamic and uncertain world. As we do so, we will work closely with industrial partners to transfer our technology to real-world applications such as warehouse robots, self-driving cars, and drone delivery systems. Canada is rapidly growing its capabilities in these areas and we hope to make big contributions in terms of the technology and people needed to shape the future.

机器人技术将在未来几年彻底改变我们社会的几个方面，从我们移动人和物的方式到医疗保健再到制造业。特别是，移动机器人技术已经成熟，从小型创业公司到世界科技巨头，各行各业都在积极追求自动驾驶汽车和无人驾驶飞行器（无人机）。虽然一些产品将在未来几年上市，但它们不会完全自主，这意味着它们不会在人类操作员可以处理的所有条件下运行。要做到这一点，还需要进行大量的研究。移动机器人的实际应用是具有挑战性的，因为操作环境（例如，我们的道路，我们的天空，大仓库）可能是巨大的，杂乱的，动态的，未知的/不确定的。这意味着关键的自治功能，包括定位、映射、规划和控制，必须解决这些挑战。在机器人技术领域，当涉及到制造可以在人类水平上运行的系统时，我们几乎没有触及表面。该研究计划的重点是将机器人的操作范围扩展到复杂和以人为中心的环境（即为人类建造和使用的环境，如我们的道路网络，城市中心，建筑物等）。该提案侧重于使机器人在有多个参与者的动态和不确定情况下安全行动并做出可靠决策的技术。将先验知识与数据结合，实现对新情况的快速适应。特别是，我们将解决(i)对动态干扰的快速适应，（ii）基于数据的运动规划，（iii）不确定环境中的性能保证，以及（iv）交互场景。这些都是开发以人为中心的环境中使用的自主性的重要因素，这些环境往往难以提前预测，并且需要机器人在其他动态参与者附近操作。作为本提案一部分的工作将包括基本的方法进步，在软件中实现新算法，以及在现实场景中进行彻底的实验测试。我们将把新的自主功能嵌入到完整的自主堆栈中，包括在动态和不确定的世界中操作移动机器人所需的所有关键功能。在此过程中，我们将与工业合作伙伴密切合作，将我们的技术应用于现实世界，如仓库机器人、自动驾驶汽车和无人机送货系统。加拿大正在迅速发展其在这些领域的能力，我们希望在塑造未来所需的技术和人才方面做出重大贡献。