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S&AS: FND: Uncertainty-Aware Safe Deep Reinforcement Learning

基本信息

批准号：
1849154
负责人：
David Held
金额：
$ 50万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-04-01 至 2023-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1849154&HistoricalAwards=false
关键词：
S&amp FND Uncertainty Aware Safe

项目摘要

Robot designers cannot always anticipate all real-world eventualities; hence robots will need to use algorithms that can learn to adapt to unexpected changes in their environment. However, if robots are to learn and adapt in the real world, they must adapt in ways that continue to maintain safety while learning. This project develops methods that ensure that robots continue to be safe even as they learn and adapt to unexpected changes in their environment. By enabling robots to be cognizant of their uncertainty, these methods can help robots operate more safely. Further, the methods will enable robots to be adaptive to unforeseen changes in their environment. These methods will be applied to autonomous driving, to enable robots to operate safely on surfaces with different levels of friction or on uneven terrain that might otherwise be unsafe to operate on. Such methods will enable safer autonomous vehicles for city driving in poor weather conditions, as well as for operating in off-road settings for search and rescue operations, patrol vehicles to detect animal poachers, and for other applications.This research develops a set of methods for uncertainty-aware safe robot learning. These methods will enable a robot to estimate the uncertainty of the effect of its actions; based on the estimated uncertainty, the robot will determine how to improve its performance while operating safely and cautiously. Furthermore, these methods will use the estimated uncertainty to determine how the robot should adapt its parameters to efficiently respond to environmental changes. The methods in this project will operate on complex policy classes such as those represented by neural networks trained with deep reinforcement learning. Such an approach will enable robots to achieve safe and adaptive long-term autonomy.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.

机器人设计师不可能总是预测到所有现实世界的可能性，因此机器人需要使用能够学习适应环境中意外变化的算法。但是，如果机器人要在真实的世界中学习和适应，它们必须以在学习的同时继续保持安全的方式进行适应。该项目开发了确保机器人持续安全的方法，即使它们学习和适应环境中的意外变化。通过使机器人能够认识到它们的不确定性，这些方法可以帮助机器人更安全地操作。此外，这些方法将使机器人能够适应环境中不可预见的变化。这些方法将应用于自动驾驶，使机器人能够在摩擦力不同的表面或不平坦的地形上安全运行，否则可能不安全。这些方法将使自动驾驶车辆在恶劣天气条件下的城市驾驶更加安全，以及在越野环境中进行搜索和救援行动，巡逻车辆检测动物偷猎者，本研究发展了一套不确定性感知的安全机器人学习方法。这些方法将使机器人能够估计其动作效果的不确定性;基于估计的不确定性，机器人将确定如何在安全和谨慎操作的同时提高其性能。此外，这些方法将使用估计的不确定性来确定机器人应该如何调整其参数以有效地响应环境变化。该项目中的方法将在复杂的策略类上运行，例如由深度强化学习训练的神经网络所代表的策略类。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。

项目成果

期刊论文数量（32）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

TAX-Pose: Task-Specific Cross-Pose Estimation for Robot Manipulation

DOI：
10.48550/arxiv.2211.09325
发表时间：
2022-11
期刊：
ArXiv
影响因子：
0
作者：
Chuer Pan;Brian Okorn;Harry Zhang;Ben Eisner;David Held
通讯作者：
Chuer Pan;Brian Okorn;Harry Zhang;Ben Eisner;David Held

Learning Off-policy for Online Planning

在线规划的离线学习

DOI：
发表时间：
2021
期刊：
Conference on Robot Learning (CoRL
影响因子：
0
作者：
Sikchi, Harshit;Zhou, Wenxuan;Held, David
通讯作者：
Held, David

Planning with Spatial and Temporal Abstraction from Point Clouds for Deformable Object Manipulation

DOI：
发表时间：
2022
期刊：
影响因子：
0
作者：
Xingyu Lin;Carl Qi;Yunchu Zhang;Zhiao Huang;Katerina Fragkiadaki;Yunzhu Li;Chuang Gan;David Held
通讯作者：
Xingyu Lin;Carl Qi;Yunchu Zhang;Zhiao Huang;Katerina Fragkiadaki;Yunzhu Li;Chuang Gan;David Held

Deep Projective Rotation Estimation through Relative Supervision