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RI: Small: Representation Learning for Semantic Mapping and Safe Robot Navigation

RI：小型：语义映射和安全机器人导航的表示学习

基本信息

批准号：
2007141
负责人：
Nikolay Atanasov
金额：
$ 44.85万
依托单位：
University of California-San Diego
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007141&HistoricalAwards=false
关键词：
RI Small Representation Learning Semantic

项目摘要

Autonomous robot systems offer tremendous potential for transforming various industry sectors, including transportation, construction, mining, and agriculture. The operational conditions in these domains, however, are unstructured and dynamically changing. This poses a major challenge for current robot system designs as they depend on static offline environment models and rigid dynamics models that do not improve with operational experience. The impact of autonomous systems in these domains is also limited by hand-designed safety rules that fail to account for the complexity and uncertainty of real-world operation. This project will develop new theoretical and algorithmic tools for advancing the ability of autonomous systems to comprehend their surroundings online from sensory observations and adapt their operation safely in response to changing conditions. On the educational front, the project aims to increase the participation of underrepresented undergraduate students in education and research activites related to robot autonomy in human environments and develop new talent in the STEM fields, which will be critical for the future of the U.S. economy.The key innovations of the project include techniques for online inference of object shapes and robot dynamics models from sensory observations as well as control design for the learned robot dynamics, subject to safety constraints from the observed objects. First, an implicit surface model of object shape, compactly encoded with a latent feature vector is proposed. An online optimization algorithm to estimate the object poses, shapes, and robot motion jointly is developed. This algorithm enables semantic environment understanding and specification of safety constraints for autonomous navigation. Second, the spectral properties of the Koopman operator and the versatility of Bayesian neural networks are leveraged to design self-supervised robot dynamics learning algorithms. These algorithms provide an adaptive way of estimating robot dynamics from online data, while relying on approximation error bounds to guarantee that the control design satisfies the safety constraints provided by the perceptual system. These innovations will enable autonomous robot operation in unknown environments that is adaptable, due to the use of learned robot and object models, and safe, due to the use of perception- and uncertainty-aware constraints in the control design.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.

自主机器人系统为改变各个行业提供了巨大的潜力，包括运输，建筑，采矿和农业。然而，这些领域的操作条件是非结构化的和动态变化的。这对当前的机器人系统设计提出了重大挑战，因为它们依赖于静态离线环境模型和刚性动力学模型，这些模型不会随着操作经验而改进。自主系统在这些领域的影响也受到手工设计的安全规则的限制，这些规则未能考虑到现实世界操作的复杂性和不确定性。该项目将开发新的理论和算法工具，以提高自主系统通过感官观察在线了解周围环境并安全地调整其操作以应对不断变化的条件的能力。在教育方面，该项目旨在增加代表性不足的本科生参与与人类环境中机器人自主性相关的教育和研究活动，并培养STEM领域的新人才，该项目的关键创新包括物体形状的在线推理技术和机器人动力学模型，以及学习机器人动力学的控制设计，受观察对象的安全约束。首先，提出了一种隐式的表面模型的物体形状，complexly编码的潜在特征向量。开发了一种在线优化算法来估计对象的位姿，形状和机器人运动联合。该算法实现了自主导航的语义环境理解和安全约束规范。其次，利用Koopman算子的谱特性和贝叶斯神经网络的多功能性来设计自监督机器人动力学学习算法。这些算法提供了一种自适应的方式，估计机器人动态从在线数据，同时依赖于近似误差界，以保证控制设计满足安全约束提供的感知系统。这些创新将使自主机器人在未知环境中运行，由于使用了学习的机器人和对象模型，并且由于在控制设计中使用了感知和不确定性感知约束，因此具有适应性。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。