Convergence Analysis and Efficient New Framework for Sampling-Based Optimal Control — NSF Award to Carnegie Mellon University (PA,
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 (nonlinea
| Award title | Convergence Analysis and Efficient New Framework for Sampling-Based Optimal Control |
|---|---|
| Award ID | 2512805 |
| Awardee | Carnegie Mellon University |
| City | PITTSBURGH |
| State | PA |
| Amount obligated | $550,000 |
| Principal investigator | Guanya Shi |
| Program | EPCN-Energy-Power-Ctrl-Netwrks |
| Start date | 09/01/2025 |
| 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 ph |
| Source | NSF Awards |
$799/mo
Try NSFGrants →