FMitF: Track I: Abstraction Refinement-guided Program Synthesis for Verifiable Robot Learn — NSF Award to Rutgers University New B
This project advances science at the intersection of robotics, artificial intelligence, and formal verification to enable reliable and transparent robot behavior in real-world settings. As robots increasingly assist with complex tasks—from warehouse logistics to supporting independent living—ensuring their safe and tru
| Award title | FMitF: Track I: Abstraction Refinement-guided Program Synthesis for Verifiable Robot Learn |
|---|---|
| Award ID | 2525293 |
| Awardee | Rutgers University New Brunswick |
| City | NEW BRUNSWICK |
| State | NJ |
| Amount obligated | $899,109 |
| Principal investigator | He Zhu |
| Program | FMitF: Formal Methods in the F |
| Start date | 09/01/2025 |
| Abstract | This project advances science at the intersection of robotics, artificial intelligence, and formal verification to enable reliable and transparent robot behavior in real-world settings. As robots increasingly assist with complex tasks—from warehouse logistics to supporting independent living—ensuring their safe and trustworthy operation is essential. However, state-of-the-art robot learning methods, such as deep reinforcement learning, rely heavily on opaque neural network controllers that are d |
| Source | NSF Awards |
$799/mo
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