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.

从我们将人们和事物转移到医疗保健到制造业的方式中，机器人技术将在未来几年内彻底改变我们社会的几个方面。特别是，移动机器人技术已经成熟到自动驾驶汽车和无人驾驶汽车（又称无人机），该行业正在积极追求从小型初创公司到世界技术巨头的行业。尽管某些产品将在未来几年内推向市场，但它们不会完全自主，这意味着它们不会在人类运营商可以处理的所有条件下运行。为了实现这一目标，需要更多的研究。 移动机器人的真实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）可能很大，混乱，动态且不知名/不确定。这意味着包括本地化，映射，计划和控制在内的关键自治功能必须应对这些挑战。在机器人技术中，当制造可以在人类层面运行的系统时，我们几乎没有刮擦表面。 该研究计划的重点是将机器人的操作封装扩展到复杂和以人为中心的环境（即，我们的道路网络，城市中心，建筑物等为人类建造和使用的环境）。该提案的重点是使机器人能够安全地行动并在动态和不确定的情况下与多个参与者做出可靠的决策的技术。先验知识与数据相结合，以快速适应新情况。特别是，我们将解决（i）快速适应动态干扰，（ii）基于数据的运动计划，（iii）在不确定环境中的性能保证以及（iv）交互式方案。这些都是开发自主权以在以人为本环境中使用的重要要素，在以人为中心的环境中，这些要素往往会提前不可预测，并且要求机器人在其他动态参与者附近操作。作为本提案的一部分的工作将包括基本方法学进步，软件中新型算法的实施以及在现实情况下进行彻底的实验测试。我们将把新的自主权功能嵌入完整的自治堆栈中，其中包括通过动态和不确定的世界操作移动机器人所需的所有关键功能。这样做，我们将与工业合作伙伴紧密合作，将我们的技术转移到现实世界中的应用程序，例如仓库机器人，自动驾驶汽车和无人机交付系统。加拿大在这些领域迅速增强了其能力，我们希望在塑造未来所需的技术和人们方面做出巨大贡献。