Opportunity number
NSF 18-557
National Science Foundation (NSF)
Smart Autonomous System (S&AS)
Due date
June 01, 2020
Research Transportation
Project funding
Foundational projects (10 - 15) from $300,000 to $600,000, up to 3 years. Integrative projects (5 - 10) from $500,000 to $1,000,000, up to 4 years.
Program funding
Funding size
Up to $5M

Smart and Autonomous Systems (S&AS) program

RFP Summary provided by the agency

The Smart and Autonomous Systems (S&AS) program focuses on Intelligent Physical Systems (IPS) that are capable of robust, long-term autonomy requiring minimal or no human operator intervention in the face of uncertain, unanticipated, and dynamically changing situations. IPS are systems that combine perception, cognition, communication, and actuation to operate in the physical world. Examples include, but are not limited to, robotic platforms, self-driving vehicles, underwater exploration vehicles, and smart grids.

Most current IPS operate in pre-programmed ways and in a limited variety of contexts. They are incapable of handling novel situations, or of even understanding when they are outside their areas of expertise. To achieve robust, long-term autonomy, however, future IPS need to be aware of their capabilities and limitations and to adapt their behaviors to compensate for limitations and/or changing conditions.

To foster such intelligent systems, the S&AS program supports research in four main aspects of IPS: cognizant, taskable, adaptive, and ethical. Cognizant IPS exhibit high-level awareness of their own capabilities and limitations, anticipating potential failures and re-planning accordingly. Taskable IPS can interpret high-level, possibly vague, instructions, planning out and executing concrete actions that are dependent on the particular context in which the system is operating. Adaptive IPS can change their behaviors over time, learning from their own experiences and those of other entities, such as other IPS or humans, and from instruction or observation. Ethical IPS should adhere to a system of societal and legal rules, taking those rules into account when making decisions. Each of these research areas requires the IPS to be knowledge-rich, employing a variety of representation and reasoning mechanisms, such as semantic, probabilistic, commonsense, and meta-reasoning.

What is the mission and focus of the program: research, social, economic or others?

The goal of the Smart and Autonomous Systems (S&AS) program is to promote fundamental research into Intelligent Physical Systems (IPS) that can act autonomously and reliably in a variety of situations and environments. NSF defines IPS as systems that use high-level cognition to perceive, communicate, and act in complex physical environments. Such systems should be able to act reliably despite uncertainty in perception and actuation; deal robustly with unexpected and unanticipated events; effectively handle variations in their environments, tasks, and even their own physical capabilities; and adapt to changing circumstances. IPS should be able to use and improve models of themselves and their environments that are incomplete or even inaccurate. They should incorporate societal values into their reasoning, taking into account the tradeoffs between adherence to an ethical system of societal and legal rules and the need to achieve their tasks.

How do you submit to this opportunity?

Proposers may opt to submit proposals in response to this Program Solicitation via Grants.gov or via the NSF FastLane system.

Who are the target applicants: cities, universities, companies, small business, nonprofits, or others?

Institutions of Higher Education (IHEs) – Two- and four-year IHEs (including community colleges) accredited in, and having a campus located in the US, acting on behalf of their faculty members. Special Instructions for International Branch Campuses of US IHEs: If the proposal includes funding to be provided to an international branch campus of a US institution of higher education (including through use of subawards and consultant arrangements), the proposer must explain the benefit(s) to the project of performance at the international branch campus, and justify why the project activities cannot be performed at the US campus.
Non-profit, non-academic organizations: Independent museums, observatories, research labs, professional societies and similar organizations in the U.S. associated with educational or research activities.

Example project(s) summaries from past RFPs:

S&AS: INT: COLLAB: Autonomy as a Service. Awarded Amount to Date: $329,999. This project will address this issue by letting the autonomous robots be available to the user in an on-demand manner through a novel ‘Autonomy as a service’ framework. To realize this idea, new tools will be developed for (i) describing the tasks in a way that can be understood by the robots, (ii) ensuring that the robots stay safe while executing the tasks, and (iii) methods for the robots to learn and improve over time in combination with the ability to assess their performance. The broader impact from the project will include implications for environmental monitoring, outreach programs for increasing STEM participation, and an integration of the research findings into the curriculum at the three participating institutions (Georgia Tech, BU, and MIT).

In detail, the three main research themes are: (i) From Specification to Execution: The users must be able to recruit and task the robots with new missions, which calls for formally correct ways of going from high-level specifications, formulated as Linear Temporal Logic formulae, to coordinated control programs for the robots to execute. (ii) Resilient Autonomy: When delivering a system that can be commanded to perform tasks over long periods of time, the first concern must be to preserve the integrity of the system itself, i.e., basic functionality must be ensured even as the robot team is recruited to perform a particular set of tasks. This project will achieve this through the use of composable barrier certificates that ensure the forward invariance of the safe set, i.e., if the robots start safe, they will stay safe. (iii) Trajectory Based Learning from Massive Data Sets: The agent team must be able to assess the performance of whatever it is that they are monitoring. In this project, this will be achieved through models that can be effectively learned from massive data sets through novel tools for data compression and representation. https://www.nsf.gov/awardsearch/showAward?AWD_ID=1724058&HistoricalAwards=false

(ii) Example project(s) summaries from past RFPs:

S&AS: INT: Inference, Reasoning, and Learning for Robust Autonomous Driving: Awarded Amount to Date: $1,398,587. Led by researchers in Mechanical and Aerospace Engineering, and Computer Science at Cornell University, the goal of this research is to develop, integrate and validate theory and algorithms to enable robust and persistent autonomous driving. This project is aligned with NSF’s Intelligent Physical Systems (IPS) because the algorithms will require cognizant and reflective capabilities in a knowledge-rich environment. The technical approach will develop a robust perceptual pipeline for detection, scene estimation, prediction, and anomaly/mistake detection and learning; integrate the algorithms into Cornell’s autonomous car software framework and validate the components and system in a series of experimental scenarios to enable their faster adoption by the community. Key component level algorithms to be developed include anytime deep learning detectors with quantifiable performance; multiple hypothesis reasoning with memory attributes; generalized probabilistic anticipation algorithms to mimic a human’s mental model of a dynamic scene; and anomaly/mistake detection coupled with online learning. Outcomes will include open source algorithms and data logs; publications, conferences, workshops; data logs for open ended projects in courses and across the community; and undergrad and high school education and diversity programs in the interdisciplinary area of autonomous driving. https://www.nsf.gov/awardsearch/showAward?AWD_ID=1724282&HistoricalAwards=false

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