# Convergence Analysis and Efficient New Framework for Sampling-Based Optimal Control

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · Carnegie Mellon University (PA) · $550,000

## Abstract

Modern AI & robotics systems, such as general-purpose humanoid robots, must make intelligent decisions in real time, in unstructured environments. At the heart of these capabilities lies a fundamental problem: How can we compute optimal actions when such robotic systems are governed by highly complex dynamics (nonlinear, even discontinuous)? Traditional optimal control tools, while powerful, often fall short—they can be slow, unreliable, or rely on overly simplified assumptions about the real physics. This project aims to advance a promising category of methods known as Sampling-Based Optimal Control, which has recently gained popularity due to its flexibility (can easily handle complex systems), scalability (can leverage massively parallel computation on GPUs), and empirical success in solving complex robotic planning and control problems. However, these methods currently lack a solid theoretical foundation and systematic design principles. This project will fill that gap by developing new mathematical frameworks to understand, analyze, and improve these method—eventually leading to more reliable, efficient, and intelligent decision-making methods for real-world robotic and autonomous systems. By bridging theory and practice, this work supports NSF’s mission to promote transformative research in AI & robotics, and has the potential to impact a wide range of fields where autonomous systems must operate reliably and effectively in the real world.

Optimal control for complex

## Key facts

- **NSF award ID:** 2512805
- **Awardee organization:** Carnegie Mellon University (PA)
- **SAM.gov UEI:** U3NKNFLNQ613
- **PI:** Guanya Shi
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Control systems & applications, CONTROL SYSTEMS, LEARNING & INTELLIGENT SYSTEMS
- **Estimated total:** $550,000
- **Funds obligated:** $550,000
- **Transaction type:** Standard Grant
- **Period:** 09/01/2025 → 08/31/2028

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2512805

## Citation

> US National Science Foundation, Award 2512805, Convergence Analysis and Efficient New Framework for Sampling-Based Optimal Control. Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nsf/2512805. Licensed CC0.

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