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（包括运输，建筑，采矿和农业）提供了巨大的潜力。但是，这些域中的操作条件是非结构化和动态变化的。这对当前机器人系统设计构成了重大挑战，因为它们依赖于静态的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善。自主系统在这些域中的影响也受到手工设计的安全规则的限制，这些规则无法解决现实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，以提高自主系统从感官观察中理解周围环境的能力，并根据不断变化的条件安全地调整其操作。在教育方面，该项目旨在增加代表性不足的本科生参与教育和研究活动与人类环境中的机器人自主权有关的活动，并在STEM领域发展新人才，这对于美国经济的未来至关重要。从观察到的对象。首先，提出了一个隐式对象形状的表面模型，并提出了用潜在特征向量编码的对象形状。共同开发了一种在线优化算法，以估计对象提出，形状和机器人运动。该算法使语义环境可以理解和规范自动导航的安全限制。其次，将Koopman操作员的光谱特性和贝叶斯神经网络的多功能性利用来设计自我监督的机器人动力学学习算法。这些算法提供了一种自适应方法来估算在线数据中的机器人动态，同时依靠近似误差界限来确保控制设计满足感知系统提供的安全性约束。这些创新将在未知的环境中实现自动驾驶机器人操作，由于使用了学识渊博的机器人和对象模型，由于使用了感知和不确定性的约束在控制设计中，因此可以适应，并且安全，这奖反映了NSF的法定任务，并通过评估基础的智力效果和宽阔的范围来评估，并被视为值得通过评估的支持。