Collaborative Research: SHF: Small: Interactive Synthesis and Repair For Robot Programs
合作研究:SHF:小型:机器人程序的交互式合成和修复
基本信息
- 批准号:2006404
- 负责人:
- 金额:$ 25万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-06-15 至 2023-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Over the past few years, robots have started to be deployed in unstructured human environments. There are hundreds of robots deployed in hospitals, hotels, and supermarkets. Unfortunately, the software that runs on robots is programmed using low-level abstractions and languages, and is hard to transfer across robots and environments. In addition robotic software requires complex control logic to ensure that robots are safe and well-behaved in all situations. Thus, robot software is extraordinarily hard to write and maintain. This research project develops tools and techniques to make robot software safer, easier to write, and easier to maintain. The intellectual merits of the project are the development of (1) techniques for fixing bugs in robot software, based on advances to automatic program repair and program synthesis; (2) abstractions for writing robot software that can automatically handle certain kinds of failures, based on new programming-language design; (3) methods for checking the correctness of robot software, based on new program-verification techniques. The project's broader significance and importance are that it helps make robot software easier to write and maintain, and cheaper, safer, and more reliable. The project encourages further research at the intersection of programming languages and robotics by publishing research results and releasing open-source software. The project also involves high-school outreach workshops to broaden participation in computing.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.
在过去的几年里,机器人已经开始部署在非结构化的人类环境中。医院、酒店和超市中部署了数百台机器人。不幸的是,在机器人上运行的软件是使用低级抽象和语言编程的,很难在机器人和环境之间传输。此外,机器人软件需要复杂的控制逻辑,以确保机器人在所有情况下都是安全和行为良好的。因此,机器人软件非常难以编写和维护。这个研究项目开发工具和技术,使机器人软件更安全,更容易编写,更容易维护。该项目的智力价值是:(1)基于自动程序修复和程序合成的进步,开发修复机器人软件中错误的技术;(2)基于新的编程语言设计,编写能够自动处理某些类型故障的机器人软件的抽象;(3)基于新的程序验证技术,检查机器人软件正确性的方法。该项目更广泛的意义和重要性在于,它有助于使机器人软件更容易编写和维护,更便宜,更安全,更可靠。该项目通过发布研究成果和发布开源软件,鼓励在编程语言和机器人技术的交叉领域进行进一步研究。该项目还包括高中外展讲习班,以扩大参与计算。该奖项反映了NSF的法定使命,并已被认为是值得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的支持。
项目成果
期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Iterative Program Synthesis for Adaptable Social Navigation
适应性社交导航的迭代程序综合
- DOI:
- 发表时间:2021
- 期刊:
- 影响因子:0
- 作者:Holtz, Jarrett;Andrews, Simon;Guha, Arjun;Biswas, Joydeep
- 通讯作者:Biswas, Joydeep
SocialGym: A Framework for Benchmarking Social Robot Navigation
- DOI:10.1109/iros47612.2022.9982021
- 发表时间:2021-09
- 期刊:
- 影响因子:0
- 作者:Jarrett Holtz;Joydeep Biswas
- 通讯作者:Jarrett Holtz;Joydeep Biswas
Robot Action Selection Learning via Layered Dimension Informed Program Synthesis
- DOI:
- 发表时间:2020-08
- 期刊:
- 影响因子:0
- 作者:Jarrett Holtz;Arjun Guha;Joydeep Biswas
- 通讯作者:Jarrett Holtz;Arjun Guha;Joydeep Biswas
IV-SLAM: Introspective Vision for Simultaneous Localization and Mapping
- DOI:
- 发表时间:2020-08
- 期刊:
- 影响因子:0
- 作者:Sadegh Rabiee;Joydeep Biswas
- 通讯作者:Sadegh Rabiee;Joydeep Biswas
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Joydeep Biswas其他文献
The Quest For "Always-On" Autonomous Mobile Robots
追求“永远在线”的自主移动机器人
- DOI:
10.24963/ijcai.2019/893 - 发表时间:
2019 - 期刊:
- 影响因子:0
- 作者:
Joydeep Biswas - 通讯作者:
Joydeep Biswas
Five Years of SSL-Vision - Impact and Development
SSL-Vision 五年 - 影响与发展
- DOI:
- 发表时间:
2013 - 期刊:
- 影响因子:0
- 作者:
S. Zickler;Tim Laue;José Angelo Gurzoni;Oliver Birbach;Joydeep Biswas;M. Veloso - 通讯作者:
M. Veloso
SOCIALGYM 2.0: Simulator for Multi-Robot Learning and Navigation in Shared Human Spaces
SOCIALGYM 2.0:共享人类空间中的多机器人学习和导航模拟器
- DOI:
10.1609/aaai.v38i21.30562 - 发表时间:
2024 - 期刊:
- 影响因子:1.8
- 作者:
Rohan Chandra;Zayne Sprague;Joydeep Biswas - 通讯作者:
Joydeep Biswas
Learning to Optimize Autonomy in Competence-Aware Systems
学习优化能力感知系统中的自主性
- DOI:
- 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
Connor Basich;Justin Svegliato;K. H. Wray;S. Witwicki;Joydeep Biswas;S. Zilberstein - 通讯作者:
S. Zilberstein
Competence-aware systems
能力感知系统
- DOI:
10.1016/j.artint.2022.103844 - 发表时间:
2023-03-01 - 期刊:
- 影响因子:4.600
- 作者:
Connor Basich;Justin Svegliato;Kyle H. Wray;Stefan Witwicki;Joydeep Biswas;Shlomo Zilberstein - 通讯作者:
Shlomo Zilberstein
Joydeep Biswas的其他文献
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{{ truncateString('Joydeep Biswas', 18)}}的其他基金
CAREER: Robust Perception and Customization for Long-Term Autonomous Mobile Service Robots
职业:长期自主移动服务机器人的鲁棒感知和定制
- 批准号:
2046955 - 财政年份:2021
- 资助金额:
$ 25万 - 项目类别:
Standard Grant
Collaborative Research: RI: Medium: Introspective Perception and Planning for Long-Term Autonomy
合作研究:RI:中:长期自治的内省感知和规划
- 批准号:
1954778 - 财政年份:2020
- 资助金额:
$ 25万 - 项目类别:
Continuing Grant
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