# CAREER: Toward Guaranteed Reliability in AI-Enabled Robotic Teleoperation Through Formal Logic and Certification

> **NSF 01002627DB NSF RESEARCH & RELATED ACTIVIT** · University of California-Riverside (CA) · $573,377

## Abstract

This project will create a safe and reliable way for people and robots to work together through teleoperation (remote control of machines), especially in high-risk settings such as surgery, disaster response, and space or nuclear operations. In these environments, robots must perform precise tasks while responding to human guidance. However, many current artificial intelligence approaches are difficult to understand and verify, which limits trust and wider use in safety-critical applications. This project will develop new methods that will help robots better understand human intent, provide timely assistance, and maintain safe and predictable performance, even in uncertain situations, allowing humans and robots to share control more effectively while reducing workload. The broader impacts of this project will include more trustworthy and reliable robotic systems for healthcare, manufacturing, and exploration in space and the ocean. The project will also promote education in science, technology, engineering, and mathematics by providing hands-on research opportunities and training for undergraduate and graduate students, engaging K–12 students in interactive robotics activities, and collaborating with national laboratories and industry partners to establish safety standards and encourage the responsible use of advanced robotic systems. 

This project seeks to develop a unified framework for safe, reliable, and interpretable teleoperation, advancing the collaboration between humans and robots in high-risk, unstructured environments. The research integrates AI foundation models with formal methods for safety specification, enabling robotic systems to interpret human intent, adapt to dynamic conditions, and maintain predictable behavior even under uncertainty.  Unlike existing teleoperation approaches, which purely rely on opaque AI “black-box” models or oversimplified safety assumptions, this work focuses on producing certifiable and generalizable methods that move b

## Key facts

- **NSF award ID:** 2541947
- **Awardee organization:** University of California-Riverside (CA)
- **SAM.gov UEI:** MR5QC5FCAVH5
- **PI:** MINGYU CAI
- **Primary program:** 01002627DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Artificial Intelligence (AI), CAREER-Faculty Erly Career Dev, CYBER-PHYSICAL SYSTEMS (CPS)
- **Estimated total:** $573,377
- **Funds obligated:** $573,377
- **Transaction type:** Standard Grant
- **Period:** 06/01/2026 → 05/31/2031

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2541947

## Citation

> US National Science Foundation, Award 2541947, CAREER: Toward Guaranteed Reliability in AI-Enabled Robotic Teleoperation Through Formal Logic and Certification. Retrieved via AI Analytics 2026-07-20 from https://api.ai-analytics.org/grant/nsf/2541947. Licensed CC0.

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